Stratechery by Ben Thompson (original) (raw)

In the days after SpaceX’s awe-inspiring Starship launch-and-catch — watch the first eight minutes of this video if you haven’t yet — there was another older video floating around on X, this time of Richard Bowles, a former executive at Arianespace, the European rocket company. The event was the Singapore Satellite Industry Forum, and the year was 2013:

This morning, SpaceX came along and said, “We foresee a launch costing 7million”.Well,ok,let’signorethe7,let’ssay7 million”. Well, ok, let’s ignore the 7, let’s say 7million”.Well,ok,let’signorethe7,let’ssay15 million…at 15millioneveryoperatorwouldchangetheirgameplanecompletely.Everysupplierwouldchangetheirgameplancompletely.Wewouldn’tbebuildingsatellitesexactlyaswearetoday,soalotofthesequestionsIthinkitmightbeinterestingtogoonthatandsay,“Wheredoyouseeyourcompaniesifyou’regoingtocompetewitha15 million every operator would change their gameplane completely. Every supplier would change their gameplan completely. We wouldn’t be building satellites exactly as we are today, so a lot of these questions I think it might be interesting to go on that and say, “Where do you see your companies if you’re going to compete with a 15millioneveryoperatorwouldchangetheirgameplanecompletely.Everysupplierwouldchangetheirgameplancompletely.Wewouldn’tbebuildingsatellitesexactlyaswearetoday,soalotofthesequestionsIthinkitmightbeinterestingtogoonthatandsay,“Wheredoyouseeyourcompaniesifyou’regoingtocompetewitha15 million launch program.” So Richard, where do you see your company competing with a $15 million launch?”…

RB: SpaceX is an interesting phenomenon. We saw it, and you just mentioned it, I thought it was 5millionor5 million or 5millionor7 million…

Why don’t you take Arianespace instead of SpaceX first. Where would you compete with a $15 million launch?

RB: I’ve got to talk about what I’m competing with, because that then predicates exactly how we will compete when we analyze what we are competing with. Obviously we like to analyze the competition.

So today, SpaceX hasn’t launched into the geosynchronous orbit yet, they’re doing very well, their progress is going forward amazingly well, but I’m discovering in the market is that SpaceX primarily seems to be selling a dream, which is good, we should all dream, but I think a 5millionlaunch,ora5 million launch, or a 5millionlaunch,ora15 million launch, is a bit of a dream. Personally I think reusability is a dream. Recently I was at a session where I was told that there was no recovery plan because they’re not going to have any failures, so I think that’s a part of the dream.

So at the moment, I feel that we’re looking, and you’re presenting to me, how am I going to respond to a dream? My answer to respond to a dream is that first of all, you don’t wake people up, they have to wake up on their own, and then once the market has woken up to the dream and the reality, then we’ll compete with that.

But they are looking at a price which is about half yours today.

RB: It’s a dream.

Alright. Suppose that you wake up and they’re there, what would you Arianespace do.

RB: We would have to react to it. They’re not supermen, so whatever they can do we can do. We would then have to follow. But today, at the moment…it is a theoretical question at this moment in time.

I personally don’t believe it’s going to be theoretical for that much longer. They’ve done everything almost they said they would do. That’s true.

The moderator ended up winning the day; in 2020 Elon Musk said on a podcast that the “best case” for Falcon 9 launches was indeed 15million(i.e.mostcostmore,butthatpricepointhadbeenachieved).Ofcoursecustomerspayalotmore:SpaceXchargesaretailpriceof15 million (i.e. most cost more, but that price point had been achieved). Of course customers pay a lot more: SpaceX charges a retail price of 15million(i.e.mostcostmore,butthatpricepointhadbeenachieved).Ofcoursecustomerspayalotmore:SpaceXchargesaretailpriceof67 million per launch, in part because it has no competition; Arianespace retired the Ariane 5 rocket, which had a retail launch price of $178 million, in 2023. Ariane 6 had its first launch this year, but it’s not price competitive, in part because it’s not reusable. From Politico:

The idea of copying SpaceX and making Ariane partly reusable was considered and rejected. That decision haunts France’s Economy Minister Bruno Le Maire. “In 2014 there was a fork in the road, and we didn’t take the right path,” Le Maire said in 2020.

But just because it works for Elon, doesn’t make it good for Europe. Once it’s up and running, Ariane 6 should have nine launches a year — of which around four will be for institutional missions, like government reconnaissance satellites and earth observation systems. The rest will be targeted at commercial clients.

Compare that to SpaceX. Fed by a steady stream of Pentagon and industry contracts, in addition to missions for its own Starlink satellite constellation, Musk’s company carried out a record 96 launches in 2023.

“It wasn’t that we just said reusability is bullshit,” said [former head the European Space Agency Jan] Wörner of the early talks around Ariane 6 in the mid-2010s, and the consideration of building reusable stages rather than burning through fresh components each mission. “If you have 10 flights per year and you are only building one new launcher per year then from an industrial point of view that’s not going to work.”

Wörner’s statement is like Bowles in the way in which it sees the world as static; Bowles couldn’t see ahead to a world where SpaceX actually figured out how to reuse rockets by landing them on drone ships, much less the version 2 example of catching a much larger rocket that we saw this weekend. Wörner, meanwhile, can’t see backwards: the reason why SpaceX has so much more volume, both from external customers and from itself (Starlink), is because it is cheap. Cheapness creates scale, which makes things even cheaper, and the ultimate output is entirely new markets.

The SpaceX Dream

Of course Bowles was right in another way: SpaceX is a dream. It’s a dream of going to Mars, and beyond, of extending humanity’s reach beyond our home planet; Arianespace is just a business. That, though, has been their undoing. A business carefully evaluates options, and doesn’t necessarily choose the highest upside one, but rather the one with the largest expected value, a calculation that incorporates the likelihood of success — and even then most find it prudent to hedge, or build in option value.

A dreamer, though, starts with success, and works backwards. In this case, Musk explained the motivation for driving down launch costs on X:

First off, this made it imperative that SpaceX find a way to launch a massively larger rocket that is fully recoverable, and doesn’t include the weight and logistics costs of the previous approach (this weekend SpaceX caught the Super Heavy booster; the next step is catching the Starship spacecraft that sits above it). Once SpaceX can launch massively larger rockets cheaply, though, it can start to do other things, like dramatically expand Starlink capability.

The next generation Starlink satellites, which are so big that only Starship can launch them, will allow for a 10X increase in bandwidth and, with the reduced altitude, faster latency https://t.co/HLYdjjia3o

— Elon Musk (@elonmusk) October 14, 2024

Starlink won’t be the only beneficiary; the Singapore moderator had it right back in 2013: everyone will change their gameplan completely, which will mean more business for SpaceX, which will only make things cheaper, which will mean even more business. Indeed, there is a window to rocketports that don’t have anything to do with Mars, but simply facilitate drastically faster transportation here on planet earth. The transformative possibilities of scale — and the dramatic decrease in price that follows — are both real and hard to imagine.

Tesla’s Robotaxi Presentation

The Starship triumph wasn’t the only Musk-related story of the week: last Thursday Tesla held its We, Robot event where it promised to unveil its Robotaxi, and observers were considerably less impressed. From Bloomberg:

Elon Musk unveiled Tesla Inc.’s highly anticipated self-driving taxi at a flashy event that was light on specifics, sending its stock sliding as investors questioned how the carmaker will achieve its ambitious goals. The chief executive officer showed off prototypes of a slick two-door sedan called the Cybercab late Thursday, along with a van concept and an updated version of Tesla’s humanoid robot. The robotaxi — which has no steering wheel or pedals — could cost less than $30,000 and “probably” will go into production in 2026, Musk said.

The product launch, held on a movie studio lot near Los Angeles, didn’t address how Tesla will make the leap from selling advanced driver-assistance features to fully autonomous vehicles. Musk’s presentation lacked technical details and glossed over topics including regulation or whether the company will own and operate its own fleet of Cybercabs. As Jefferies analysts put it, Tesla’s robotaxi appears “toothless.”

The underwhelming event sent Tesla’s shares tumbling as much as 10% Friday in New York, the biggest intraday decline in more than two months. They were down 7.6% at 12:29 p.m., wiping out $58 billion in market value. The stock had soared almost 70% since mid-April, largely in anticipation of the event. Uber Technologies Inc. and Lyft Inc., competing ride-hailing companies whose investors had been nervously awaiting the Cybercab’s debut, each surged as much as 11% Friday. Uber’s stock hit an all-time high.

Tesla has a track record of blowing past timelines Musk has offered for all manner of future products, and has had a particularly difficult time following through on his self-driving forecasts. The CEO told investors in 2019 that Tesla would have more than 1 million robotaxis on the road by the following year. The company hasn’t deployed a single autonomous vehicle in the years since.

First off, the shockingly short presentation — 22:44 from start to “Let’s get the party started” — was indeed devoid of any details about the Robotaxi business case. Secondly, all of the criticisms of Musk’s mistaken predictions about self-driving are absolutely true. Moreover, the fact of the matter is that Tesla is now far behind the current state-of-the-art, Waymo, which is in operation in four U.S. cities and about to start up in two more. Waymo has achieved Level 4 automation, while Tesla’s are stuck at Level 2. To review the levels of automation:

• Level 0: Limited features that provide warnings and momentary assistance (i.e. automatic emergency braking)
• Level 1: Steering or brake/acceleration automation (i.e. cruise control or lane centering)
• Level 2: Steering and brake/acceleration control, which must be constantly supervised (i.e. hands-on-wheel)
• Level 3: Self-driving that only operates under pre-defined conditions, and in which the driver must take control immediately when requested
• Level 4: Self-driving that only operates under pre-defined conditions, under which the driver is not expected to take control
• Level 5: Self-driving under all conditions, with no expectation of driver control

Waymo has two big advantages relative to Tesla: first, its cars have a dramatically more expansive sensor suite, including camera, radar, and LiDAR; the latter is the most accurate way to measure depth, which is particularly tricky for cameras and fairly imprecise for radar. Second, any Waymo car can be taken over by a remote driver any time it encounters a problem. This doesn’t happen often — once every 17,311 miles in sunny California last year — but it is comforting to know that there is a fallback.

The challenge is that both of these advantages cost money: LiDAR is the biggest reason why the Generation 5 Waymo’s on the streets of San Francisco cost a reported $200,000; Generation 6 has fewer sensors and should be considerably cheaper, and prices will come down as Waymo scales, but this is still a barrier. Humans in data centers, meanwhile, sitting poised to take over a car that encounters trouble, are not just a cost center but also a limit on scalability. Then again, higher cost structures are its own limitation on scalability; Waymos are awesome but they will need to get an order of magnitude cheaper to change the world.

The Autonomy Dream

What was notable about Musk’s Tesla presentation is what it actually did include. Start with that last point; Musk’s focus was on that changing the world bit:

You see a lot of sci-fi movies where the future is dark and dismal. It’s not a future you want to be in. I love Bladerunner, but I don’t know if we want that future. I think we want that duster he’s wearing, but not the bleak apocalypse. We want to have a fun, exciting future that if you could look in a crystal ball and see the future, you’d be like “Yes, I wish that I could be there now”. That’s what we want.

Musk proceeded to talk about having a lounge on wheels that gave you your time back and was safer to boot, and which didn’t need ugly parking lots; the keynote slides added parks to LAX and Sofi and Dodger Stadiums:

One of the things that is really interesting is how will this affect the cities that we live in. When you drive around a city, or the car drive you around the city, you see that there’s a lot of parking lots. There’s parking lots everywhere. There are parking garages. So what would happen if you have an autonomous world is that you can now turn parking lots into parks…there’s a lot of opportunity to create greenspace in the cities that we live in.

This is certainly an attractive vision; it’s also far beyond the world of Uber and Lyft or even Waymo, which are focused on solving the world as it actually exists today. That means dealing with human drivers, which means there will be parking lots for a long time to come. Musk’s vision is a dream.

What, though, would that dream require, if it were to come true? Musk said it himself: full autonomy provided by a fleet of low cost vehicles that make it silly — or prohibitively expensive, thanks to sky-rocketing insurance — for anyone to drive themselves. That isn’t Level 4, like Waymo, it’s Level 5, and, just as importantly, it’s cheap, because cheap drives scale and scale drives change.

Tesla’s strategy for “cheap” is well-known: the company eschews LiDAR, and removed radar from new models a few years ago, claiming that it would accomplish its goals using cameras alone.[1](#fn1-13727 "Although there does appear to be an inactive radar module in recent Models S and X; radar is comparatively cheap, and is particularly useful in low visibility situations where both cameras and LiDAR struggle") Setting aside the viability of this claim, the connection to the the dream is clear: a cameras-only approach enables the low cost vehicles integral to Musk’s dream. Yes, Waymo equipment costs will come down with scale, but Waymo’s current approach is both safer in the present and also more limited in bringing about the future.

What many folks seemed to miss in Musk’s presentation was his explanation as to how Tesla — and only Tesla — might get there.

The Bitter Lesson

Rich Sutton wrote one of the most important and provocative articles about AI in 2019; it’s called The Bitter Lesson:

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore’s law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation. There were many examples of AI researchers’ belated learning of this bitter lesson, and it is instructive to review some of the most prominent.

The examples Sutton goes over includes chess, where search beat deterministic programming, and Go, where unsupervised learning did the same. In both cases bringing massive amounts of compute to bear was both simpler and more effective than humans trying to encode their own shortcuts and heuristics. The same thing happened with speech recognition and computer vision: deep learning massively outperforms any sort of human-guided algorithms. Sutton notes towards the end:

One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.

It’s a brilliant observation, to which I might humbly add one additional component: while the Bitter Lesson is predicated on there being an ever-increasing amount of compute, which reliably solves once-intractable problems, one of the lessons of LLMs is that you also need an ever-increasing amount of data. Existing models are already trained on all of the data AI labs can get their hands on, including most of the Internet, YouTube transcripts, scanned books, etc.; there is much talk about creating synthetic data, both from humans and from other LLMs, to ensure that scaling laws continue. The alternative is that we hit the so-called “data wall”.

LLMs, meanwhile, are commonly thought about in terms of language — it is in the name, after all — but what they actually predict are tokens, and tokens can be anything, including driving data. Timothy Lee explained some of Waymo’s research in this area at Understanding AI:

Any self-driving system needs an ability to predict the actions of other vehicles. For example, consider this driving scene I borrowed from a Waymo research paper:

Vehicle A wants to turn left, but it needs to do it without running into cars B or D. There are a number of plausible ways for this scene to unfold. Maybe B will slow down and let A turn. Maybe B will proceed, D will slow down, and A will squeeze in between them. Maybe A will wait for both vehicles to pass before making the turn. A’s actions depend on what B and D do, and C’s actions, in turn, depend on what A does.

If you are driving any of these four vehicles, you need to be able to predict where the other vehicles are likely to be one, two, and three seconds from now. Doing this is the job of the prediction module of a self-driving stack. Its goal is to output a series of predictions that look like this:

Researchers at Waymo and elsewhere struggled to model interactions like this in a realistic way. It’s not just that each individual vehicle is affected by a complex set of factors that are difficult to translate into computer code. Each vehicle’s actions depend on the actions of other vehicles. So as the number of cars increases, the computational complexity of the problem grows exponentially.

But then Waymo discovered that transformer-based networks were a good way to solve this kind of problem.

“In driving scenarios, road users may be likened to participants in a constant dialogue, continuously exchanging a dynamic series of actions and reactions mirroring the fluidity of communication,” Waymo researchers wrote in a 2023 research paper.

Just as a language model outputs a series of tokens representing text, Waymo’s vehicle prediction model outputs a series of tokens representing vehicle trajectories—things like “maintain speed and direction,” “turn 5 degrees left,” or “slow down by 3 mph”.

Rather than trying to explicitly formulate a series of rules for vehicles to follow (like “stay in your lane” and “don’t hit other vehicles”), Waymo trained the model like an LLM. The model learned the rules of driving by trying to predict the trajectories of human-driven vehicles on real roads.

This data-driven approach allowed the model to learn subtleties of vehicle interactions that are not described in any driver manual and would be hard to capture with explicit computer code.

This is not yet a panacea. Lee notes later in his article:

One big problem Sinavski noted is that Wayve hasn’t found a vision-language model that’s “really good at spatial reasoning.” If you’re a long-time reader of Understanding AI, you might remember when I asked leading LLMs to tell the time from an analog clock or solve a maze. ChatGPT, Claude, and Gemini all failed because today’s foundation models are not good at thinking geometrically.

This seems like it would be a big downside for a model that’s supposed to drive a car. And I suspect it’s why Waymo’s perception system isn’t just one big network. Waymo still uses traditional computer code to divide the driving scene up into discrete objects and compute a numerical bounding box for each one. This kind of pre-processing gives the prediction network a head start as it reasons about what will happen next.

Another concern is that the opaque internals of LLMs make them difficult to debug. If a self-driving system makes a mistake, engineers want to be able to look under the hood and figure out what happened. That’s much easier to do in a system like Waymo’s, where some of the basic data structures (like the list of scene elements and their bounding boxes) were designed by human engineers.

But the broader point here is that self-driving companies do not face a binary choice between hand-crafted code or one big end-to-end network. The optimal self-driving architecture is likely to be a mix of different approaches. Companies will need to learn the best division of labor from trial and error.

That sounds right, but for one thing: The Bitter Lesson. To go back to Sutton:

This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

If The Bitter Lesson ends up applying to true Level 5 autonomy, then Waymo is already signed up for school. A “mix of different approaches” clearly works better now, and may for the next few years, but does it get them to Level 5? And what of the data, to the extent it is essential to the The Sweet Outcome of self-taught AI? This was the part of the Tesla presentation I referenced above:

One of the reasons why the computer can be so much better than a person is that we have millions of cars that are training on driving. It’s like living millions of lives simultaneously and seeing very unusal situations that a person in their entire lifetime would not see, hopefully. With that amount of training data, it’s obviously going to be much better than what a human could be, because you can’t live a million lives. It can also see in all directions simultaneously, and it doesn’t get tired or text or any of those things, so it will naturally be 10x, 20x, 30x safer than a human for all those reasons.

I want to emphasize that the solution that we have is AI and vision. There’s no expensive equipment needed. The Model 3 and Model Y and S and X that we make today will be capable of full autonomy unsupervised. And that means that our costs of producing the vehicle is low.

Again, Musk has been over-promising and under-delivering in terms of self-driving for existing Tesla owners for years now, so the jury is very much out on whether current cars get full unsupervised autonomy. But that doesn’t change the fact that those cars do have cameras, and those cameras are capturing data and doing fine-tuning right now, at a scale that Waymo has no way of matching. This is what I think Andrej Karpathy, the former Tesla Autopilot head, was referring to in his recent appearance on the No Priors podcast:

I think people think that Waymo is ahead of Tesla, I think personally Tesla is ahead of Waymo, and I know it doesn’t look like that, but I’m still very bullish on Tesla and it’s self-driving program. I think that Tesla has a software problem, and I think Waymo has a hardware problem, is the way I put it, and I think software problems are much easier. Tesla has deployment of all these cars on earth at scale, and I think Waymo needs to get there. The moment Tesla gets to the point where they can actually deploy and it actually works I think is going to be really incredible…

I’m not sure that people are appreciating that Tesla actually does use a lot of expensive sensors, they just do it at training time. So there a bunch of cars that drive around with LiDARS, they do a bunch of stuff that doesn’t scale and they have extra sensors etc., and they do mapping and all this stuff. You’re doing it at training time and then you’re distilling that into a test-time package that is deployed to the cars and is vision only. It’s like an arbitrage on sensors and expense. And so I think it’s actually kind of a brilliant strategy that I don’t think is fully appreciated, and I think is going to work out well, because the pixels have the information, and I think the network will be capable of doing that, and yes at training time I think these sensors are really useful, but I don’t think they are as useful at test time.

Do note that Karpathy — who worked at Tesla for five years — is hardly a neutral observer, and also note that he forecasts a fully neural net approach to driving as taking ten years; that’s hardly next year, as Musk promised. That end goal, though, is Level 5, with low cost sensors and thus low cost cars, the key ingredient of realizing the dream of full autonomy and the transformation that would follow.

The Cost of Dreams

I don’t, for the record, know if the Tesla approach is going to work; my experience with both Waymo and Tesla definitely makes clear that Waymo is ahead right now (and the disengagement numbers for Tesla are multiple orders of magnitude worse). Most experts assume that LiDAR sensors are non-negotiable in particular.

The Tesla bet, though, is that Waymo’s approach ultimately doesn’t scale and isn’t generalizable to true Level 5, while starting with the dream — true autonomy — leads Tesla down a better path of relying on nothing but AI, fueled by data and fine-tuning that you can only do if you already have millions of cars on the road. That is the connection to SpaceX and what happened this weekend: if you start with the dream, then understand the cost structure necessary to achieve that dream, you force yourself down the only path possible, forgoing easier solutions that don’t scale for fantastical ones that do.

1. Although there does appear to be an inactive radar module in recent Models S and X; radar is comparatively cheap, and is particularly useful in low visibility situations where both cameras and LiDAR struggle ↩

This Article is available as a video essay on YouTube

The popular history of technology usually starts with the personal computer, and for good reason: that was the first high tech device that most people ever used. The only thing more impressive than the sheer audacity of “A computer on every desk and in every home” as a corporate goal, is the fact that Microsoft accomplished it, with help from its longtime competitor Apple.

In fact, though, the personal computer wave was the 2nd wave of technology, particularly in terms of large enterprises. The first wave — and arguably the more important wave, in terms of economic impact — was the digitization of back-end offices. These were real jobs that existed:

These are bookkeepers and and tellers at a bank in 1908; fast forward three decades and technology had advanced:

The caption for this 1936 Getty Images photo is fascinating:

The new system of maintaining checking accounts in the National Safety Bank and Trust Company, of New York, known as the “Checkmaster,” was so well received that the bank has had to increase its staff and equipment. Instead of maintaining a mininum balance, the depositor is charged a small service charge for each entry on his statement. To date, the bank has attracted over 30,000 active accounts. Bookkeepers are shown as they post entries on “Checkmaster” accounts.

It’s striking how the first response to a process change is to come up with a business model predicated on covering new marginal costs; only later do companies tend to consider the larger picture, like how low-marginal-cost checking accounts might lead to more business for the bank overall, the volume of which can be supported thanks to said new technology.

Jump ahead another three decades and the back office of a bank looked like this:

Now the image is color and all of the workers are women, but what is perhaps surprising is that this image is, despite all of the technological change that had happened to date, particularly in terms of typewriters and calculators, not that different from the first two.

However, this 1970 picture was one of the last images of its kind: by the time this picture was taken, Bank of America, where this picture was taken, was already well on its way to transitioning all of its accounting and bookkeeping to computers; most of corporate America soon followed, with the two primary applications being accounting and enterprise resource planning. Those were jobs that had been primarily done by hand; now they were done by computers, and the hands were no longer needed.

Tech’s Two Philosophies

In 2018 I described Tech’s Two Philosophies using the four biggest consumer tech companies: Google and Facebook were on one side, and Apple and Microsoft on the other.

In Google’s view, computers help you get things done — and save you time — by doing things for you. Duplex was the most impressive example — a computer talking on the phone for you — but the general concept applied to many of Google’s other demonstrations, particularly those predicated on AI: Google Photos will not only sort and tag your photos, but now propose specific edits; Google News will find your news for you, and Maps will find you new restaurants and shops in your neighborhood. And, appropriately enough, the keynote closed with a presentation from Waymo, which will drive you…

Zuckerberg, as so often seems to be the case with Facebook, comes across as a somewhat more fervent and definitely more creepy version of Google: not only does Facebook want to do things for you, it wants to do things its chief executive explicitly says would not be done otherwise. The Messianic fervor that seems to have overtaken Zuckerberg in the last year, though, simply means that Facebook has adopted a more extreme version of the same philosophy that guides Google: computers doing things for people.

Google and Facebook’s approach made sense given their position as Aggregators, which “attract end users by virtue of their inherent usefulness.” Apple and Microsoft, on the other hand, were platforms born in an earlier age of computing:

This is technology’s second philosophy, and it is orthogonal to the other: the expectation is not that the computer does your work for you, but rather that the computer enables you to do your work better and more efficiently. And, with this philosophy, comes a different take on responsibility. Pichai, in the opening of Google’s keynote, acknowledged that “we feel a deep sense of responsibility to get this right”, but inherent in that statement is the centrality of Google generally and the direct culpability of its managers. Nadella, on the other hand, insists that responsibility lies with the tech industry collectively, and all of us who seek to leverage it individually…

This second philosophy, that computers are an aid to humans, not their replacement, is the older of the two; its greatest proponent — prophet, if you will — was Microsoft’s greatest rival, and his analogy of choice was, coincidentally enough, about transportation as well. Not a car, but a bicycle:

You can see the outlines of this philosophy in these companies’ approaches to AI. Google is the most advanced, thanks to the way it saw the obvious application of AI to its Aggregation products, particularly search and advertising. Facebook, now Meta, has made major strides over the last few years as it has overhauled its recommendation algorithms and advertising products to also be probabilistic, both in response to the rise of TikTok in terms of customer attention, and the severing of their deterministic ad product by Apple’s App Tracking Transparency initiative. In both cases their position as Aggregators compelled them to unilaterally go out and give people stuff to look at.

Apple, meanwhile, is leaning heavily into Apple Intelligence, but I think there is a reason its latest ad campaign feels a bit weird, above-and-beyond the fact it is advertising a feature that is not yet available to non-beta customers. Apple is associated with jewel-like devices that you hold in your hand and software that is accessible to normal people; asking your phone to rescue a fish funeral with a slideshow feels at odds with Steve Jobs making a movie on stage during the launch of iMovie:

That right there is a man riding a bike.

Microsoft Copilot

Microsoft is meanwhile — to the extent you count their increasingly fraught partnership with OpenAI — in the lead technically as well. Their initial product focus for AI, however, is decidedly on the side of being a tool to, as their latest motto states, “empower every person and every organization on the planet to achieve more.”

CEO Satya Nadella said in the recent pre-recorded keynote announcing Copilot Wave 2:

You can think of Copilot as the UI for AI. It helps you break down these siloes between your work artifacts, your communications, and your business processes. And we’re just getting started. In fact, with scaling laws, as AI becomes more capable and even agentic, the models themselves become more of a commodity, and all the value gets created by you how steer, ground, fine-tune these models with your business data and workflow. And how it composes with the UI layer of human to AI to human interaction becomes critical.

Today we’re announcing Wave 2 of Microsoft 365 Copilot. You’ll see us evolve Copilot in three major ways: first, it’s about bringing the web plus work plus pages together as the new AI system for knowledge work. With Pages we’ll show you how Copilot can take any information from the web or your work and turn it into a multiplayer AI-powered canvas. You can ideate with AI and collaborate with other people. It’s just magical. Just like the PC birthed office productivity tools as we know them today, and the web makes those canvases collaborative, every platform shift has changed work artifacts in fundamental ways, and Pages is the first new artifact for the AI age.

Notice that Nadella, like most pop historians (including yours truly!), is reaching out to draw a link to the personal computer, but here the relevant personal computer history casts more shadow than light onto Nadella’s analogy. The initial wave of personal computers were little more than toys, including the Commodore 64 and TRS-80, sold in your local Radio Shack; the Apple I, released in 1976, was initially sold as a bare circuit board:

A year later Apple released the Apple II; now there was a case, but you needed to bring your own TV:

Two years later and Apple II had a killer app that would presage the movement of personal computers into the workplace: VisiCalc, the first spreadsheet.

VisiCalc’s utility for business was obvious — in fact, it was conceived of by Dan Bricklin while watching a lecture at Harvard Business School. That utility, though, was not about running business critical software like accounting or ERP systems; rather, an employee with an Apple II and VisiCalc could take the initiative to model their business and understand how things worked at a view grounded in a level of calculation that was both too much for one person, yet not sufficient to hire an army of backroom employees, or, increasingly at that point, reserve time on the mainframe.

Notice, though, how this aligned with the Apple and Microsoft philosophy of building tools: tools are meant to be used, but they take volition to maximize their utility. This, I think, is a challenge when it comes to Copilot usage: even before Copilot came out employees with initiative were figuring out how to use other AI tools to do their work more effectively. The idea of Copilot is that you can have an even better AI tool — thanks to the fact it has integrated the information in the “Microsoft Graph” — and make it widely available to your workforce to make that workforce more productive.

To put it another way, the real challenge for Copilot is that it is a change management problem: it’s one thing to charge $30/month on a per-seat basis to make an amazing new way to work available to all of your employees; it’s another thing entirely — a much more difficult thing — to get all of your employees to change the way they work in order to benefit from your investment, and to make Copilot Pages the “new artifact for the AI age”, in line with the spreadsheet in the personal computer age.

Clippy and Copilot

Salesforce CEO Marc Benioff was considerably less generous towards Copilot in last week’s Dreamforce keynote. After framing machine learning as “Wave 1” of AI, Benioff said that Copilots were Wave 2, and from Microsoft’s perspective it went downhill from there:

We moved into this Copilot world, but the Copilot world has been kind of a hit-and-miss world. The Copilot world where customers have said to us “Hey, I got these Copilots but they’re not exactly performing as we want them to. We don’t see how that Copilot world is going to get us to the real vision of artificial intelligence of augmentation of productivity, of better business results that we’ve been looking for. We just don’t see Copilot as that key step for our future.” In some ways, they kind of looked at Copilot as the new Microsoft Clippy, and I get that.

The Clippy comparison was mean but not entirely unfair, particularly in the context of users who don’t know enough to operate with volition. Former Microsoft executive Steven Sinofsky explained in Hard Core Software:

Why was Microsoft going through all this and making these risky, or even edgy, products? Many seemed puzzled by this at the time. In order to understand that today, one must recognize that using a PC in the early 1990s (and before) was not just difficult, but it was also confusing, frustrating, inscrutable, and by and large entirely inaccessible to most everyone unless you had to learn how to use one for work.

Clippy was to be a replacement for the “Office guru” people consulted when they wanted to do things in Microsoft Office that they knew were possible, but were impossible to discover; Sinofsky admits that a critical error was making Clippy too helpful with simple tasks, like observing “Looks like you’re trying to write a letter” when you typed “Dear John” and hit return. Sinofsky reflected:

The journey of Clippy (in spite of our best efforts that was what the feature came to be called) was one that parallels the PC for me in so many ways. It was not simply a failed feature, or that back-handed compliment of a feature that was simply too early like so many Microsoft features. Rather Clippy represented a final attempt at trying to fix the desktop metaphor for typical or normal people so they could use a computer.

What everyone came to realize was that the PC was a generational change and that for those growing up with a PC, it was just another arbitrary and random device in life that one just used. As we would learn, kids didn’t need different software. They just needed access to a PC. Once they had a PC they would make cooler, faster, and more fun documents with Office than we were. It was kids that loved WordArt and the new graphics in Word and PowerPoint, and they used them easily and more frequently than Boomers or Gen X trying to map typewriters to what a computer could do.

It was not the complexity that was slowing people down, but the real concern that the wrong thing could undo hours of work. Kids did not have that fear (yet). We needed to worry less about dumbing the software down and more about how more complex things could get done in a way that had far less risk.

This is a critical insight when it comes to AI, Copilot, and the concept of change management: a small subset of Gen Xers and Boomers may have invented the personal computer, but for the rest of their cohort it was something they only used if they had to (resentfully), and only then the narrow set of functionality that was required to do their job. It was the later generations that grew up with the personal computer, and hardly give inserting a table or graphic into a document a second thought (if, in fact, they even know what a “document” is). For a millenial using a personal computer doesn’t take volition; it’s just a fact of life.

Again, though, computing didn’t start with the personal computer, but rather with the replacement of the back office. Or, to put it in rather more dire terms, the initial value in computing wasn’t created by helping Boomers do their job more efficiently, but rather by replacing entire swathes of them completely.

Agents and o1

Benioff implicitly agrees; the Copilot Clippy insult was a preamble to a discussion of agents:

But it was pushing us, and they were trying to say, what is the next step? And we are now really at that moment. That is why this show is our most important Dreamforce ever. There’s no question this is the most exciting Dreamforce and the most important Dreamforce. What you’re going to see at this show is technology like you have never seen before…The first time you build and deploy your first autonomous agent for your company that is going to help you to be more productive, to augment your employees, and to get these better business results, you’re going to remember that like the first time it was in y our Waymo. This is the 3rd wave of AI. It’s agents…

Agents aren’t copilots; they are replacements. They do work in place of humans — think call centers and the like, to start — and they have all of the advantages of software: always available, and scalable up-and-down with demand.

We know that workforces are overwhelmed. They’re doing these low-value tasks. They’ve got kind of a whole different thing post-pandemic. Productivity is at a different place. Capacity is at a different place…we do see that workforces are different, and we realize that 41% of the time seems to be wasted on low value and repetitive tasks, and we want to address that. The customers are expecting more: zero hold times, to be more personal and empathetic, to work with an expert all the time, to instantly schedule things. That’s our vision, our dream for these agents…

What if these workforces had no limits at all? Wow. That’s kind of a strange thought, but a big one. You start to put all of these things together, and you go, we can kind of build another kind of company. We can build a different kind of technology platforms. We can take the Salesforce technology platform that we already have, and that all of you have invested so much into, the Salesforce Platform, and we can deliver the next capability. The next capability that’s going to make our companies more productive. To make our employees more augmented. And just to deliver much better business results. That is what Agentforce is.

This Article isn’t about the viability of Agentforce; I’m somewhat skeptical, at least in the short term, for reasons I will get to in a moment. Rather, the key part is the last few sentences: Benioff isn’t talking about making employees more productive, but rather companies; the verb that applies to employees is “augmented”, which sounds much nicer than “replaced”; the ultimate goal is stated as well: business results. That right there is tech’s third philosophy: improving the bottom line for large enterprises.

Notice how well this framing applies to the mainframe wave of computing: accounting and ERP software made companies more productive and drove positive business results; the employees that were “augmented” were managers who got far more accurate reports much more quickly, while the employees who used to do that work were replaced. Critically, the decision about whether or not make this change did not depend on rank-and-file employees changing how they worked, but for executives to decide to take the plunge.

The Consumerization of IT

When Benioff founded Salesforce in 1999, he came up with a counterintuitive logo:

Of course Salesforce was software; what it was not was SOFTWARE, like that sold by his previous employer Oracle, which at that time meant painful installation and migrations that could take years, and even then would often fail. Salesforce was different: it was a cloud application that you never needed to install or update; you could simply subscribe.

Cloud-based software-as-a-service companies are the norm now, thanks in part to Benioff’s vision. And, just as Salesforce started out primarily serving — you guessed it! — sales forces, SaaS applications can focus on individual segments of a company. Indeed, one of the big trends over the last decade were SaaS applications that grew, at least in the early days, through word-of-mouth and trialing by individuals or team leaders; after all, all you needed to get started was a credit card — and if there was a freemium model, not even that!

This trend was part of a larger one, the so-called “consumerization of IT”. Douglas Neal and John Taylor, who first coined the term in 2001, wrote in a 2004 Position Paper:

Companies must treat users as consumers, encouraging employee responsibility, ownership and trust by providing choice, simplicity and service. The parent/child attitude that many IT departments have traditionally taken toward end users is now obsolete.

This is actually another way of saying what Sinofsky did: enterprise IT customers, i.e. company employees, no longer needed to be taught how to use a computer; they grew up with them, and expected computers to work the same way their consumer devices did. Moreover, the volume of consumer devices meant that innovation would now come from that side of technology, and the best way for enterprises to keep up would be to ideally adopt consumer infrastructure, and barring that, seek to be similarly easy-to-use.

It’s possible this is how AI plays out; it is what has happened to date, as large models like those built by OpenAI or Anthropic or Google or Meta are trained on publicly available data, and then are available to be fine-tuned for enterprise-specific use cases. The limitation in this approach, though, is the human one: you need employees who have the volition to use AI with the inherent problems introduced by this approach, including bad data, hallucinations, security concerns, etc. This is manageable as long as a motivated human is in the loop; what seems unlikely to me is any sort of autonomous agent actually operating in a way that makes a company more efficient without an extensive amount of oversight that ends up making the entire endeavor more expensive.

Moreover, in the case of Agentforce specifically, and other agent initiatives more generally, I am unconvinced as to how viable and scalable the infrastructure necessary to manage auto-regressive large language models will end up being. I got into some of the challenges in this Update:

The big challenge for traditional LLMs is that they are path-dependent; while they can consider the puzzle as a whole, as soon as they commit to a particular guess they are locked in, and doomed to failure. This is a fundamental weakness of what are known as “auto-regressive large language models”, which to date, is all of them.

To grossly simplify, a large language model generates a token (usually a word, or part of a word) based on all of the tokens that preceded the token being generated; the specific token is the most statistically likely next possible token derived from the model’s training (this also gets complicated, as the “temperature” of the output determines what level of randomness goes into choosing from the best possible options; a low temperature chooses the most likely next token, while a higher temperature is more “creative”). The key thing to understand, though, is that this is a serial process: once a token is generated it influences what token is generated next.

The problem with this approach is that it is possible that, in the context of something like a crossword puzzle, the token that is generated is wrong; if that token is wrong, it makes it more likely that the next token is wrong too. And, of course, even if the first token is right, the second token could be wrong anyways, influencing the third token, etc. Ever larger models can reduce the likelihood that a particular token is wrong, but the possibility always exists, which is to say that auto-regressive LLMs inevitably trend towards not just errors but compounding ones.

Note that these problems exist even with specialized prompting like insisting that the LLM “go step-by-step” or “break this problem down into component pieces”; they are still serial output machines that, once they get something wrong, are doomed to deliver an incorrect answer. At the same time, this is also fine for a lot of applications, like writing; where the problem manifests itself is with anything requiring logic or iterative reasoning. In this case, a sufficiently complex crossword puzzle suffices.

That Update was about OpenAI’s new o1 model, which I think is a step change in terms of the viability of agents; the example I used in that Update was solving a crossword puzzle, which can’t be done in one-shot — but can be done by o1.

o1 is explicitly trained on how to solve problems, and second, o1 is designed to generate multiple problem-solving streams at inference time, choose the best one, and iterate through each step in the process when it realizes it made a mistake. That’s why it got the crossword puzzle right — it just took a really long time.

o1 introduces a new vector of potential improvement: while auto-regressive LLMs scaled in quality with training set size (and thus the amount of compute necessary), o1 scales inference. This image is from OpenAI’s announcement page:

This second image is a potential problem in a Copilot paradigm: sure, a smarter model potentially makes your employees more productive, but those increases in productivity have to be balanced by both greater inference costs and more time spent waiting for the model (o1 is significantly slower than a model like 4o). However, the agent equation, where you are talking about replacing a worker, is dramatically different: there the cost umbrella is absolutely massive, because even the most expensive model is a lot cheaper, above-and-beyond the other benefits like always being available and being scalable in number.

More importantly, scaling compute is exactly what the technology industry is good at. The one common thread from Wave 1 of computing through the PC through SaaS and consumerization of IT, is that problems gated by compute are solved not via premature optimizations but via the progression of processing power. The key challenge is knowing what to scale, and I believe OpenAI has demonstrated the architecture that will benefit from exactly that.

Data and Palantir

That leaves the data piece, and while Benioff bragged about all of the data that Salesforce had, it doesn’t have everything, and what it does have is scattered across the phalanx of applications and storage layers that make up the Salesforce Platform. Indeed, Microsoft faces the same problem: while their Copilot vision includes APIs for 3rd-party “agents” — in this case, data from other companies — the reality is that an effective Agent — i.e. a worker replacement — needs access to everything in a way that it can reason over. The ability of large language models to handle unstructured data is revolutionary, but the fact remains that better data still results in better output; explicit step-by-step reasoning data, for example, is a big part of how o1 works.

To that end, the company I am most intrigued by, for what I think will be the first wave of AI, is Palantir. I didn’t fully understand the company until this 2023 interview with CTO Shyam Sankar and Head of Global Commercial Ted Mabrey; I suggest reading or listening to the whole thing, but I wanted to call out this exchange in particular:

Was there an aha moment where you have this concept — you use this phrase now at the beginning of all your financial reports, which is that you’re the operating system for enterprises. Now, obviously this is still the government era, but it’s interesting the S-1 uses that line, but it’s further down, it’s not the lead thing. Was that something that emerged later or was this that, “No, we have to be the interface for everything” idea in place from the beginning?

Shyam Sankar: I think the critical part of it was really realizing that we had built the original product presupposing that our customers had data integrated, that we could focus on the analytics that came subsequent to having your data integrated. I feel like that founding trauma was realizing that actually everyone claims that their data is integrated, but it is a complete mess and that actually the much more interesting and valuable part of our business was developing technologies that allowed us to productize data integration, instead of having it be like a five-year never ending consulting project, so that we could do the thing we actually started our business to do.

That integration looks like this illustration from the company’s webpage for Foundry, what they call “The Ontology-Powered Operating System for the Modern Enterprise”:

What is notable about this illustration is just how deeply Palantir needs to get into an enterprise’s operations to achieve its goals. This isn’t a consumery-SaaS application that your team leader puts on their credit card; it is SOFTWARE of the sort that Salesforce sought to move beyond.

If, however, you believe that AI is not just the next step in computing, but rather an entirely new paradigm, then it makes sense that enterprise solutions may be back to the future. We are already seeing that that is the case in terms of user behavior: the relationship of most employees to AI is like the relationship of most corporate employees to PCs in the 1980s; sure, they’ll use it if they have to, but they don’t want to transform how they work. That will fall on the next generation.

Executives, however, want the benefit of AI now, and I think that benefit will, like the first wave of computing, come from replacing humans, not making them more efficient. And that, by extension, will mean top-down years-long initiatives that are justified by the massive business results that will follow. That also means that go-to-market motions and business models will change: instead of reactive sales from organic growth, successful AI companies will need to go in from the top. And, instead of per-seat licenses, we may end up with something more akin to “seat-replacement” licenses (Salesforce, notably, will charge $2 per call completed by one of its agents). Services and integration teams will also make a comeback. It’s notable that this has been a consistent criticism of Palantir’s model, but I think that comes from a viewpoint colored by SaaS; the idea of years-long engagements would be much more familiar to tech executives and investors from forty years ago.

Enterprise Philosophy

Most historically-driven AI analogies usually come from the Internet, and understandably so: that was both an epochal change and also much fresher in our collective memories. My core contention here, however, is that AI truly is a new way of computing, and that means the better analogies are to computing itself. Transformers are the transistor, and mainframes are today’s models. The GUI is, arguably, still TBD.

To the extent that is right, then, the biggest opportunity is in top-down enterprise implementations. The enterprise philosophy is older than the two consumer philosophies I wrote about previously: its motivation is not the user, but the buyer, who wants to increase revenue and cut costs, and will be brutally rational about how to achieve that (including running expected value calculations on agents making mistakes). That will be the only way to justify the compute necessary to scale out agentic capabilities, and to do the years of work necessary to get data in a state where humans can be replaced. The bottom line benefits — the essence of enterprise philosophy — will compel just that.

And, by extension, we may be waiting longer than we expect for AI to take over the consumer space, at least at the scale of something like the smartphone or social media. That is good news for the MKBHDs of everything — the users with volition — but for everyone else the biggest payoff will probably be in areas like entertainment and gaming. True consumerization of AI will be left to the next generation who will have never known a world without it.

This Article is available as a video essay on YouTube

I was born in 1980, which technically makes me a part of Generation X; there is at least one data point, though, that makes me a millennial. In 2021 Fast Company conducted a survey about what constituted middle age; millennials defined it as 35 to 50, while Generation X said 45 to 55 (Baby Boomers said 45 to 60).

For my part, I wrote that Apple was in its middle age in 2018, when the company was 42 years old, a solidly millennial definition. My argument then was that Apple was increasingly — and rightly — behaving like an incumbent in a market that was no longer growing. That meant that future iPhone growth would come from (1) more expensive iPhones, (2) selling more devices to existing iPhone users (AirPods, Watches, etc.), and (3) accelerating the revenue it derived from services for iPhone users.

That is exactly what the company did over the ensuing few years, but 2022 brought a bit of a surprise: Apple, in the face of the worst inflation in a generation, declined to raise the price of the iPhone, which in real terms meant an iPhone Pro, nominally priced at 999,was999, was 999,was116 cheaper than it had been two years prior.

I thought this was a big deal at the time; I wrote in The Services iPhone:

This doesn’t make much sense for the product company Apple has always been thought to be, and doesn’t fully align with the approach I laid out in Apple’s Middle Age. It does, though, make all kinds of sense for a services company, which is focused first-and-foremost on increasing its install base. Indeed, this is the missing piece from that Update I wrote about Apple’s changing metrics. To measure its business based on users, not products, was to measure like a services company; to lower the prices of the products that lead to services revenue is to price like one.

Here’s another aspect of getting old: it’s hard to let go of the preconceptions of your youth. In yesterday’s Update I wrote that I believed that Apple would surely raise prices this year; that 999iPhoneProisnow999 iPhone Pro is now 999iPhoneProisnow177 cheaper than in 2020 in real terms, and given the fact that Apple was increasing the bill of materials on the lower end phones in particular (bumping them to 8GB RAM, and onto the latest TSMC process), surely they wouldn’t accept the hit on margins on top of the loss in value of their longstanding iPhone pricing.

In fact, though, I had it right in 2022: Apple held the line on prices once again; I should have realized the company — like myself! — really is in a different stage.

The iPhone Event

The lack of a price increase for the iPhone 16 Pro made more sense when I watched Apple’s presentation; I found the updates over the iPhone 15 Pro to be pretty underwhelming. The A18 Pro chip is on TSMC’s newest 3nm process, there is a new Camera Control button, and the screen is a bit bigger with bezels that are a bit smaller; that’s really about it from a hardware perspective, although as always, Apple continues to push the envelope with computational photography. And, frankly, that’s fine: last year’s iPhone Pro 15, the first with titanium and USB-C, was for me the iPhone I had been waiting for (and most customers don’t upgrade every year, so these and other previous updates will be new features for them).

What I find much more compelling — and arguably the best deal in iPhone history — is the $799 iPhone 16 (non-Pro). The A18 chip appears to be a binned version of the A18 Pro (there is one less GPU and smaller caches), while the aforementioned bump to 8GB of RAM — necessary to run Apple Intelligence — matches the iPhone 16 Pro. There is one fewer camera, but the two-camera system that remains has been reconfigured to a much more aesthetically pleasing pill shape that has the added bonus of making it possible to record spatial video when held horizontally. The screen isn’t quite as good, and the bezels are a bit larger, but the colors are more fun. It’s a great phone, and the closest the regular iPhone has been to the Pro since the line separated in 2017.

It’s also the more important phone when it comes to Apple’s long-term addressable market. The non-pro iPhones are the ones that stay on sale for years (the iPhone 14 just received its expected price drop to $599); one potential consideration for Apple in terms of price is that it wants to move the 8GB RAM iPhone 16 down the line sooner rather than later; a price raise, if it meant keeping a 6GB RAM iPhone for sale one year longer, could be bad for Apple Intelligence.

Apple Intelligence, meanwhile, is the great hope when it comes to driving new iPhone sales: Apple shareholders are hoping for an AI-driven “supercycle”, when consumers, eager for Apple Intelligence features, update their iPhones to one of the latest models with sufficient RAM to run Apple’s new tentpole offering.

Apple’s Crossing Lines

Notice, though, that this hope itself speaks to how Apple is at a different stage in life: the big hardware changes this year are at the low end; one of the takeaways from AI is that we are well and truly into the age of software-driven differentiation. This is hardly a surprise; I wrote in 2016’s Everything as a Service:

Apple has arguably perfected the manufacturing model: most of the company’s corporate employees are employed in California in the design and marketing of iconic devices that are created in Chinese factories built and run to Apple’s exacting standards (including a substantial number of employees on site), and then transported all over the world to consumers eager for best-in-class smartphones, tablets, computers, and smartwatches.

What makes this model so effective — and so profitable — is that Apple has differentiated its otherwise commoditizable hardware with software. Software is a completely new type of good in that it is both infinitely differentiable yet infinitely copyable; this means that any piece of software is both completely unique yet has unlimited supply, leading to a theoretical price of $0. However, by combining the differentiable qualities of software with hardware that requires real assets and commodities to manufacture, Apple is able to charge an incredible premium for its products.

The results speak for themselves: this past “down” quarter saw Apple rake in 50.6billioninrevenueand50.6 billion in revenue and 50.6billioninrevenueand10.5 billion in profit. Over the last nine years the iPhone alone has generated 600billioninrevenueandnearly600 billion in revenue and nearly 600billioninrevenueandnearly250 billion in gross profit. It is probably the most valuable — the “best”, at least from a business perspective — manufactured product of all time.

In the eight years since then the iPhone has driven another 1.4trillioninrevenueandsomethingaround1.4 trillion in revenue and something around 1.4trillioninrevenueandsomethingaround625 billion in profit;1 it is still tops in the company for both. It is software, though, specifically services revenue that doesn’t depend on how good the underlying hardware is, that is 2nd place in terms of revenue:

The comparison is even more striking when you look at profit (again, with the assumption that iPhones have a 45% gross profit margin):

Jason Snell already noted the impending convergence of the overall product and services profit lines at Six Colors:

But what about the bottom line? While Apple’s Services gross margin was 74% last quarter, products was only a measly 35%. (I’m kidding—35% margin on hardware is actually a really great number. It just can’t compare to Services, because hardware has some fundamental costs that services just don’t.)

Let’s look at total profits:

Last quarter, Apple made about 22billioninprofitfromproductsand22 billion in profit from products and 22billioninprofitfromproductsand18 billion from Services. It’s the closest those two lines have ever come to each other.

This is what was buzzing in the back of my head as I was going over all the numbers on Thursday. We’re not quite there yet, but it’s hard to imagine that there won’t be a quarter in the next year or so in which Apple reports more total profit on Services than on products.

When that happens, is Apple still a products company? Or has it crossed some invisible line?

Snell concludes his piece by imploring Apple to “remember where you came from”; hardware is what makes the whole thing work. I’m sympathetic to Snell’s case, because Apple the integrated hardware company is both an entity I admire and one that — as evidenced by my mistaken prediction yesterday — still looms large in my mind. Moreover, you can can (and should) make the case that Services revenue and profit is just an accounting distinction for what is just more hardware revenue and profit, because it is the latter that unlocks the former.

That, though, is why I broke out the iPhone specifically: I think the invisible line Snell talks about has already been crossed. Yes, next quarter’s iPhone numbers will jump back up thanks to seasonality, but this is now three straight years of Apple either favoring its more reliably growing Services business (by increasing the iPhone’s addressable market by lowering real prices) or admitting that it doesn’t have the pricing power that drove up product revenue in their middle age.

Boomer Apple

This is not, to be clear, an “Apple is Doomed” post; Apple hardware is still very good, and in lots of small ways that go beyond new buttons. I’ve been exploring the Android market over the past few weeks and there is a level of “it just works” that still differentiates the iPhone, particularly in terms of overall system integration (relative to the Pixel, which has a poor modem and problematic battery life) and worldwide reliability (relative to Samsung devices that are endlessly segmented in weird ways across markets, a hangup for me in particular).

Moreover, it’s hardly a condemnation for hardware to become “good enough”, and to decrease in price over time; that has been the path of basically all consumer electronics.

It’s impressive that Apple was innovative enough to resist gravity for as long as it did.

And, of course, there is the lock-in endemic to software: iMessage only works on iPhones,[2](#fn2-13460 "Although this can be worked around using services like AirMessage") there are all of the apps you have already purchased, and, the muscle memory of years in a particular ecosystem. There are Watches and AirPods that only work or work best with iPhones, and the deep integration with Macs. Apple’s chips remain best-of-breed, and the company’s investment in privacy may pay off in the AI era as people trust Apple to leverage their personal data on device and in their new private cloud.

That, though, is the rub: Craig Federighi, Apple’s software chief, usually doesn’t appear at the iPhone event; his domain is the Worldwide Developers Conference in June, and yet here he was in front of the camera for ten minutes.

Software, specifically AI, is what will drive differentiation going forward, and even in the best case scenario, where Apple’s AI efforts are good enough to keep people from switching to Google, the economics of software development push towards broad availability on every iPhone, not special features for people willing to pay a bit more. It’s as if the iPhone, which started out as one model for everyone before bifurcating into the Pro and regular (and SE) lines, is coming full circle to standardization; the difference, though, is its value is harvested through R&D intensive services that need to run everywhere, instead of unit profits driven by hardware differentiation.

So what stage of life is this, where you are banking on your earlier investments for your moat, and having no choice but to strive for good enough on the hot new trends that don’t quite fit your model? Maybe it’s the age where you’re worried about things like irregular heart beats, sleep apnea, or hearing loss. I’m not meaning to make fun of the health features that are an ever larger focus of these events; these are incredible applications of technology that Apple is right to brag about. Indeed, these features make me want to wear an Apple Watch or use AirPods.

But I’m old, and so enamored of the companies of my youth that I ignored my own (correct!) analysis of Apple’s shift in a desire to believe that this was still a product company that could wow me and charge me like it did in 2020. This is a services company now; the hardware is necessary, but insufficient for long-term growth. So it goes.

1. I am assuming 45% profit margins for the iPhone. Many iPhone models have reported gross profit margins of 50% or more, but those reports are based on the physical components of the phone, and do not include the attributed cost of software; therefore I am using 45% as a best guess to be conservative in terms of the following argument, but I would guess the true margin, including fully attributed R&D costs, is closer to 40% ↩
2. Although this can be worked around using services like AirMessage ↩

This Article is available as a video essay on YouTube

It really is a valley:

There, right in the middle of the Santa Clara Valley, formed by the Santa Cruz Mountains on the west and the Diablo Range on the east, lies the once-sleepy city of Mountain View. Mountain View was dominated by the U.S. Navy’s Moffett Field in 1955, when William Shockley, one of the inventors of the transistor at Bell Labs, returned to neighboring Palo Alto to care for his ailing mother.

Convinced that silicon was a superior material for transistors — Bell Labs was focused on germanium — Shockley, unable to hire many Bell Labs co-workers both because of the distance from New Jersey and also his abusive management style, set up the Shockley Semiconductor Laboratory in 1956 in Mountain View with a collection of young scientists. Only a year later eight of those scientists, led by Robert Noyce and Gordon Moore, fled Shockley — he really was a terrible manager — and set up Fairchild Semiconductor, a new division of Fairchild Camera and Instrument, in neighboring Sunnyvale.

It was Fairchild Semiconductor that gave the tech industry’s home the other half of its name: yes, we talk about “The Valley”, but at least when it comes to tech, we mean Silicon Valley. From TechCrunch in 2014:

As Fairchild started to grow, employees began to leave the firm to launch new spin-off businesses. Many of these firms also grew quickly, inspiring other employees still working at the company…The growth of these new companies started to reshape the region. In just 12 years, the co-founders and former employees of Fairchild generated more than 30 spin-off companies and funded many more. By 1970, chip businesses in the San Francisco area employed a total of 12,000 people…

The achievements of these companies eventually attracted attention. In 1971, a journalist named Don Hoefler wrote an article about the success of computer chip companies in the Bay Area. The firms he profiled all produced chips using silicon and were located in a large valley south of San Francisco. Hoefler put these two facts together to create a new name for the region: Silicon Valley.

Hoefler’s article and the name he coined have become quite famous, but there’s a critical part of his analysis that is often overlooked: Almost all of the silicon chip companies he profiled can be traced back to Fairchild and its co-founders.

Still, for all of the massive success downstream from Fairchild Semiconductor, none mattered more, or came to define Silicon Valley in every respect, than Intel. Arthur Rock, who had helped the so-called “Traitorous Eight” find Fairchild Camera and Instrument, funded Intel, and in the process created the compensation structure that came to define Silicon Valley. Gordon Moore wrote the roadmap for Intel — now more commonly known as Moore’s Law — which “predicted” that the number of transistors would double at a set rate, both increasing compute speed and driving down prices for that compute; “predict” is in quotes because Moore’s Law was not a physical law, but an economic one, downstream of Intel’s inexorable push for continued improvement. That, by extension, meant that Intel set the pace of innovation for all of technology, not just by making the processors for the PC — and, in an underrated wave of disruption in the early part of this century, the cloud — but also by defining the expectations of every software engineer in the entire world.

Intel’s Long Decline

Stratechery has, from the beginning, operated with a great degree of reverence for tech history; perhaps that’s why I’ve always been a part of the camp cheering for Intel to succeed. The unfortunate fact of the matter is that the need for cheerleading has been clear for as long as I have written this blog: in May 2013 I wrote that Intel needed to build out a foundry business, as the economics of their IDM business, given their mobile miss, faced long-term challenges.

Unfortunately not only did Intel not listen, but their business got a lot worse: in the late 2010’s Intel got stuck trying to move to 10nm, thanks in part to their reluctance to embrace the vastly more expensive EUV lithography process, handing the performance crown to TSMC. Meanwhile Intel’s chip design team, increasingly fat and lazy thanks to the fact they could leverage Intel’s once-industry-leading processes, had started to fall behind AMD; today AMD has both better designs and, thanks to the fact they fab their chips at TSMC, better processes. Meanwhile, the rise of hyperscalers meant there were entities that both had the scale to justify overcoming whatever software advantages Intel had, and the resources to do so; the result is that AMD has been taking data center share for years, and is on the verge of passing 50%:

[_Editor’s Note: these two paragraphs are technically incorrect, in that AMD’s data center revenue includes their AI chips; the directionally point remains, but I regret the errors_]

This chart actually understates the problem, because it only includes x86 processors; in fact, those capabilities that have allowed the hyperscalers to take advantage of AMD’s increasingly superior total cost of ownership have also been devoted to building ARM-based server chips. Amazon in particular has invested heavily in its Graviton line of chips, taking advantage of ARM’s theoretically better efficiency and lower licensing fees (as compared to Intel’s margins).

Beyond that, what is especially problematic — and why Intel’s datacenter revenue is actually down year-over-year — is that an increasing amount of data center spend is going towards AI, the latest paradigm where Intel missed the boat.

[_End Editor’s Note_]

The story Intel — or at least its past management — wants you to believe about mobile is that they foolishly passed up the opportunity to supply Apple’s iPhone, not realizing that the volume would more than make up for the margin hit; in fact, Tony Fadell told me that while Steve Jobs wanted Intel — Apple had just switched to using Intel chips for Macs — Intel chips weren’t competitive:

For me, when it came to Intel at the time, back in the mid-2000s, they were always about, “Well, we’ll just repackage what we have on the desktop for the laptop and then we’ll repackage that again for embedding.” It reminded me of Windows saying, “I’m going to do Windows and then I’m going to do Windows Mobile and I’m going to do Windows embedded.” It was using those same cores and kernels and trying to slim them down…

The mindset at Intel was never about — when they went through that CISC-RISC duality of “Which one are we going to be?”, and they chose CISC, which was the right thing at the time, if you fast forward, they also made that decision, they threw away architectural and they went to more manufacturing. That was the time when they said “We don’t have to worry about all these different product lines to meet all these architectural needs. We’re just going to have Moore’s Law take over” and so in a way that locks you into a path and that’s why Intel, not under the Pat days but previous to the Pat days, was all driven by manufacturing capability and legal. It wasn’t driven by architectural decisions, it was like, “Here’s what we got and we’re going to spread it around and we’re going to keep reusing it”.

In fact, it does go back to the Pat days, specifically CEO Pat Gelsinger’s initial stint at Intel. He was the one that pushed CISC over RISC, arguing that Intel’s CISC software advantage, supported by the company’s superior manufacturing, would ensure that the company dominated microprocessors. And, as Fadell noted, it worked, at least in PCs and servers.

Where it didn’t work was mobile: Intel couldn’t leverage its manufacturing to make x86 competitive with ARM, particularly since the latter had a head start on software; it also didn’t work in GPUs, where Intel spent years trying to build x86-based gaming chips that — you guessed it — were meant to rely on Intel’s manufacturing prowess. GPUs, of course, are the foundation of today’s AI boom, and while Intel bought Gaudi to offer AI chips, they haven’t made a dent in the market — and oh, by the way, Gaudi chips are manufactured by TSMC.

IDM 2.0

None of this story is new; I recounted it in 2021’s Intel Problems. My solution then — written shortly after Gelsinger came back to Intel, fifteen years after being passed over for the CEO job — was that the company needed to split up.

Integrating design and manufacturing was the foundation of Intel’s moat for decades, but that integration has become a strait-jacket for both sides of the business. Intel’s designs are held back by the company’s struggles in manufacturing, while its manufacturing has an incentive problem.

The key thing to understand about chips is that design has much higher margins; Nvidia, for example, has gross margins between 60~65%, while TSMC, which makes Nvidia’s chips, has gross margins closer to 50%. Intel has, as I noted above, traditionally had margins closer to Nvidia, thanks to its integration, which is why Intel’s own chips will always be a priority for its manufacturing arm. That will mean worse service for prospective customers, and less willingness to change its manufacturing approach to both accommodate customers and incorporate best-of-breed suppliers (lowering margins even further). There is also the matter of trust: would companies that compete with Intel be willing to share their designs with their competitor, particularly if that competitor is incentivized to prioritize its own business?

The only way to fix this incentive problem is to spin off Intel’s manufacturing business. Yes, it will take time to build out the customer service components necessary to work with third parties, not to mention the huge library of IP building blocks that make working with a company like TSMC (relatively) easy. But a standalone manufacturing business will have the most powerful incentive possible to make this transformation happen: the need to survive.

Two months later and Gelsinger announced his turnaround plan: IDM 2.0. Intel would separate out its manufacturing into a separate division that would serve third parties, but still under the Intel banner. Gelsinger told me in an interview that this was the only way Intel could both be competitive in chips and keep investing in the leading edge; after all, AMD’s spin-off of Global Foundries resulted in the former floundering until they could break their purchase agreements with Global Foundries and go to TSMC, and the latter giving up on the leading edge.

Gelsinger is persuasive and optimistic, and for the last three years I’ve given him the benefit of the doubt. Suddenly, though, a split is back on the table; from Bloomberg:

Intel Corp. is working with investment bankers to help navigate the most difficult period in its 56-year history, according to people familiar with the matter. The company is discussing various scenarios, including a split of its product-design and manufacturing businesses, as well as which factory projects might potentially be scrapped, said the people, who asked not to be identified because the deliberations are private…

A potential separation or sale of Intel’s foundry division, which is aimed at manufacturing chips for outside customers, would be an about-face for Chief Executive Officer Pat Gelsinger. Gelsinger has viewed the business as key to restoring Intel’s standing among chipmakers and had hoped it would eventually compete with the likes of Taiwan Semiconductor Manufacturing Co., which pioneered the foundry industry.

As the article notes, Intel is likely to consider less drastic steps first; Reuters reported that ideas include selling businesses like its Altera programmable chip business and reducing capital expeditures, including axing a proposed foundry in Germany. The company also finally killed its dividend, and is cutting 15,000 jobs, which frankly, isn’t enough; I noted in an Update last week:

Intel ended last year with 124,800 people; to put that in context, TSMC had 76,478 employees and AMD 26,000, which is to say that the two companies combined had fewer employees than Intel while making better x86 chips, an actually competitive GPU, and oh yeah, making chips for everyone else on earth, including Apple and Nvidia. A 15,000 employee cut is both too small and too late.

The fundamental problem facing the company is encapsulated in that paragraph:

• Intel doesn’t have the best manufacturing
• Intel doesn’t design the best chips
• Intel is out of the game in AI

Moreover, the future does not look bright; the problem with Intel’s most recent earnings call was threefold:

• Intel’s is technically on pace to achieve the five nodes in four years Gelsinger promised (in truth two of those nodes were iterations), but they haven’t truly scaled any of them; the first attempt to do so, with Intel 3, destroyed their margins. This isn’t a surprise: the reason why it is hard to skip steps is not just because technology advances, but because you have to actually learn on the line how to implement new technology at scale, with sustainable yield. Go back to Intel’s 10nm failure: the company could technically make a 10nm chip, they just couldn’t do so economically; there are now open questions about Intel 3, much less next year’s promised 18A.
• Intel is dramatically ramping up its Lunar Lake architecture as it is the only design the company has that is competitive with the Qualcomm ARM architecture undergirding Microsoft’s CoPilot+ PC initiative; the problem is that Lunar Lake’s tiles — including its CPU — are made by TSMC, which is both embarrassing and also terrible for margins.
• The third problem is that the goal Gelsinger has been pushing for is the aforementioned 18A, yet Intel has yet to announce a truly committed at-scale partner. Yes, the company is in talks with lots of folks and claims some number of secret agreements, but at this point the foundry strategy needs real proof points; unfortunately Intel itself ramping up on TSMC, even as it loses control of its costs, isn’t exactly a selling point as to why any third-party should put their fortunes in Intel’s hands.

All that noted, my initial response to the meltdown over Intel’s earnings was to defend Gelsinger; what is happening to Intel now is downstream of mistakes that happened years before Gelsinger came back to the company. That remains true, but Gelsinger does have one fatal flaw: he still believes in Intel, and I no longer do.

Market Realities

Here is the fundamental problem facing Intel, and by extension, U.S. dreams of controlling leading edge capacity: there is no reason for Intel Foundry to exist. Apple, Nvidia, AMD, and other leading edge fabless chip companies rely on TSMC, and why wouldn’t they? TSMC invested in EUV, surpassed Intel, and are spending tens of billions of dollars a year to continue pushing forward to 2nm and beyond. Yes, TSMC priced 3nm too low, but even if the company raises prices for future nodes, as I expect them to, the relative cost matters much less than TSMC’s superior customer services and demonstrated reliability.

The kicker is that the smartest decision for Intel’s own chip unit is to — as they are with Lunar Lake — rely on TSMC’s manufacturing as well. Intel still has advantages in PCs and a dominant position in on-premises and government data centers, but the best way to leverage those remaining areas of strength is to have TSMC make their chips.

This was, for the record, why Gelsinger did have a point in keeping the company together; Intel Foundry needs volume, and the easiest way to get that volume is from Intel itself. However, that by definition is a decision that is not driven by what is best for a theoretical Intel fabless business, but rather the impetus to restore Intel’s manufacturing capability, even as that manufacturing capability is heavily incentivized to cater to Intel’s chip business at the expense of external customers.

Gelsinger’s trump card has been the fact that TSMC is based in Taiwan, which is under continuous threat from China. Indeed, Gelsinger has been quite explicit on this point; from CNA English News in 2021:

Intel CEO Pat Gelsinger said at the Fortune Brainstorm Tech summit in California on Wednesday that the United States government should support a sustainable semiconductor supply chain in the U.S., in part because “Taiwan is not a stable place”…

Asked about the comment, TSMC Chairman Mark Liu (劉德音) said, “there’s nothing that needs to be addressed. TSMC does not speak ill of other companies in the industry,” and added there were probably not many people who believed Gelsinger’s argument. Geopolitical tensions, Liu said, may have a short-term impact, but he believed Taiwan could help create a brilliant decade for the global semiconductor industry, with the best technology and the best manufacturing ecosystem.

Gelsinger made the same point to me in that interview while explaining why Intel needed to stay together:

As we look at this, to me, there is almost a global national perspective to this, in that I deeply believe the West needs a world class technology provider, and I don’t think that splitting Intel in two, that it could survive for many, many, many years till that would become the case, that you could stand that up. Remember, given cash flows, R&D streams, products that enable us to drive that, and I’m committed to go fix it, and I think we’re on a good path to go fix it since I’ve been here as well. So for those three different reasons, we chose the IDM 2.0 path, but it’s not because we didn’t look at the alternative, it’s partially because we did.

This is where everyone who is invested in American manufacturing — or perhaps more accurately, concerned about China’s threat to Taiwan — has to get brutally honest. If the U.S. government and U.S. tech companies want to have a non-Taiwan option, they are going to have to pay for it directly. Yes, the CHIPS Act passed, but while Intel is getting a lot of funds, it’s going to take a lot more — and the price of those funds needs to be a much smarter incentive structure that drives Intel apart.

My proposal back in 2021 was purchase guarantees instead of subsidies, and I am back to thinking that is the only viable path.

That is why a federal subsidy program should operate as a purchase guarantee: the U.S. will buy A amount of U.S.-produced 5nm processors for B price; C amount of U.S. produced 3nm processors for D price; E amount of U.S. produced 2nm processors for F price; etc. This will not only give the new Intel manufacturing spin-off something to strive for, but also incentivize other companies to invest; perhaps Global Foundries will get back in the game, or TSMC will build more fabs in the U.S. And, in a world of nearly free capital, perhaps there will finally be a startup willing to take the leap.

That free capital world is gone, and it’s probably not realistic for a startup to figure out how to manufacture the most complex devices humans have ever produced; the best idea at this point is a new company that has the expertise and starting position of Intel Foundry. Critically, though, it shouldn’t be at all beholden to x86 chips, have hundreds of thousands of employees, or the cultural overhang of having once led the computing world. The best we can do is purchase guarantees — on the order of hundreds of billions of dollars over the next decade — and a prayer that someone can make such an entity stand on its own.

To summarize, there is no market-based reason for Intel Foundry to exist; that’s not a market failure in a purely economic sense, but to the extent the U.S. national security apparatus sees it as a failure is the extent to which the U.S. is going to have to pay to make it happen. And, if the U.S. is going to pay up, that means giving that foundry the best possible chance to stand on its own two feet in the long run. That means actually earning business from Apple, Nvidia, AMD, and yes, even the fabless Intel company that will remain. The tech world has moved on from Intel; the only chance for U.S. leading edge manufacturing is to do the same.

I wrote a follow-up to this Article in this Daily Update.

This Article is available as a video essay on YouTube

The original Pixel was released in 2016 at the end of interesting, at least when it came to smartphones. Two years earlier Apple had finally knocked down every barrier limiting iPhone growth, releasing the large-screen iPhone 6 worldwide, including on the elusive China Mobile; Samsung, meanwhile, had consolidated the high-end of Android. Over the ensuing decade cameras have grown in both quantity and quality, buttons and bezels have given way to dynamic islands and hidden sensors, and, most annoyingly, Android and Apple have increasingly aped the other’s aggressive App Store policies; the only thing better than a monopoly is a duopoly that reaps the same profits without the regulatory pressure.

Integration and Smartphone Innovation

What was clear is that Apple was not, as so many had predicted in the early 2010s, doomed; it’s hard to imagine now, but the conventional wisdom when I started this site was that Apple’s focus on integration, while essential to creating the modern smartphone, would lead to ever-dwindling marketshare in the face of Android’s modular approach, which, we were assured, would bring to bear more innovation at lower prices, until developers gave up on iOS completely, relegating the iPhone to the status of the mid-1990s Macintosh — i.e. on life support.

This didn’t happen for several reasons.

First off, popular history of Windows and the Mac is totally wrong; yes, the Mac came before Windows, but critically, Windows was not a new operating system: it sat on top of DOS, which both preceded the Mac by years, and was originally sold by IBM, making it the standard for enterprise. The iPhone, on the other hand, absolutely did come out before Android, which is to say it had the head start in users and developers that the Mac never had (it’s also worth pointing out that the iPhone, in contrast to the Mac, has always been the performance leader).

Second, the iPhone is first and foremost a consumer device: this means that the user experience matters, and integration, which smooths over the seams of modularization, delivers a better user experience. This was the foundation of the argument I made for the iPhone’s long-term prospects in 2013’s What Clayton Christensen Got Wrong:

The issue I have with this analysis of vertical integration — and this is exactly what I was taught at business school — is that the only considered costs are financial. But there are other, more difficult to quantify costs. Modularization incurs costs in the design and experience of using products that cannot be overcome, yet cannot be measured. Business buyers — and the analysts who study them — simply ignore them, but consumers don’t. Some consumers inherently know and value quality, look-and-feel, and attention to detail, and are willing to pay a premium that far exceeds the financial costs of being vertically integrated.

What is notable is that the iPhone’s most successful premium competitors have been more integrated than not: while Samsung and Huawei don’t make the Android operating system, they do make a huge number of components of their phones — more than Apple — which helps them keep up in the race for new and novel features.

That there is the third point: new and novel features continue to matter, because the smartphone is the black hole of consumer electronics. Or, to use the term I coined in 2013, the smart phone is Obsoletive:

In 2006, the Nokia 1600 was the top-selling phone in the world, and the BlackBerry Pearl the best-selling smartphone. Both were only a year away from their doom, but that doom was not a cheaper, less-capable product, but in fact the exact opposite: a far more powerful, and fantastically more expensive product called the iPhone.

The problem for Nokia and BlackBerry was that their specialties — calling, messaging, and email — were simply apps: one function on a general-purpose computer. A dedicated device that only did calls, or messages, or email, was simply obsolete.

An even cursory examination of tech history makes it clear that “obsoletion” — where a cheaper, single-purpose product is replaced by a more expensive, general purpose product — is just as common as “disruption” — even more so, in fact. Just a few examples (think about it, and you’ll come up with a bunch more):

• The typewriter and word processor were obsoleted by the PC
• Typesetting was obsoleted by the Mac and desktop publishing
• The newspaper was obsoleted by the Internet
• The CD player was obsoleted by the iPod
• The iPod was obsoleted by the iPhone

Smartphones and app stores have only accelerated this process, obsoleting the point-and-shoot, handheld video games, watches, calculators, maps, and many, many more.

The smartphone was, and remains, the perfect product: it is with you all the time, constantly connected, and increasingly capable and extendable. This both means that the utility-to-dollar-spent ratio is hugely positive, even for phones that cost well into the four-figures, and also continues to accentuate the advantages of integration, which makes these new capabilities possible. Google Senior Vice President of Devices & Services Rick Osterloh told me in an interview that will be posted tomorrow:

In the premium side [of the market], I think the leaders are going to end up being people with deep technical capabilities. It is the frontier space of computing in my view. And, because phones are with you all the time and they’re so heavily used, people want them to do everything. And so, there’s almost a sensational appetite for increasing capability within phones, which keeps pushing the envelope on what computing capability can you add to it to be able to accomplish the next task. And, I mean, I wouldn’t have thought a decade ago that people would ever be interested in taking continuous 4K video on this, and then being able to immediately upload it to a cloud. And, I don’t know, you wouldn’t have envisioned that necessarily.

I think now, phones are on the cusp of being able to, not only do stuff like that, but also become your wallet, become your keys, run advanced AI workloads, do stuff in the background for you. I mean, the amount of capabilities they have today is outrageous, and that’s only going to grow based on what I’m seeing now. Various times I thought maybe this work had plateaued, but that is absolutely not the case. I think they’re going to become more and more computer-like, and because they’re with you, they’ve got this place of importance that is difficult to overestimate.

In short, integration wins, at least in premium smartphones, which goes a long way in explaining why Pixel even exists: yes, Google partners closely with OEMs like Samsung, but if it ever wants to take on the iPhone, the company needs to do it all.

And yet, the Pixel hasn’t amounted to much so far: Google is up to around 5% marketshare in the U.S., but only 1% worldwide. There are various reasons this might be the case, some of which may be under Google’s control; the biggest problem, though, is the end of interesting: smartphones have gotten better over the eight years the Pixel has been in the market, but in rather boring ways; the paradigm shift that let Apple and Samsung take over the premium market happened before the Pixel ever existed. But now comes AI.

Android Primacy

Yesterday Google announced its ninth iteration of Pixel phones, and as you might expect, the focus was on AI. It is also unsurprising that the foundation of Osterloh’s pitch at the beginning of the keynote was about integration. What was notable is that the integration he focused on actually didn’t have anything to do with Pixel at all, but rather Android and Google:

We’re re-imagining the entire OS layer, putting Gemini right at the core of Android, the world’s most popular OS. You can see how we’re innovating with AI at every layer of the tech stack: from the infrastructure and the foundation models, to the OS and devices, and the apps and services you use every day. It’s a complete end-to-end experience that only Google can deliver. And I want to talk about the work we’re going to integrate it all together, with an integrated, helpful AI assistant for everyone. It changes how people interact with their mobile devices, and we’re building it right into Android.

For years, we’ve been pursuing our vision of a mobile AI assistant that you can work with like you work with a real life personal assistant, but we’ve been limited by the bounds of what existing technologies can do. So we’ve completely rebuilt the personal assistant experience around our Gemini models, creating a novel kind of computing help for the Gemini era.

The new Gemini assistant can go beyond understanding your words, to understanding your intent, so you can communicate more naturally. It can synthesize large amounts of information within seconds, and tackle complex tasks. It can draft messages for you, brainstorm with you, and give you ideas on how you can improve your work. With your permission, it can offer unparalleled personalized help, accessing relevant information across your Gmail Inbox, your Google calendar, and more. And it can reason across personal information and Google’s world knowledge, to provide just the right help and insight you need, and its only possible through advances we made in Gemini models over the last six months. It’s the biggest leap forward since we launched Google Assistant. Now we’re going to keep building responsibly, and pushing to make sure Gemini is available to everyone on every phone, and of course this starts with Android.

This may seem obvious, and in many respects it is: Google is a services company, which means it is incentivized to serve the entire world, maximizing the leverage on its costs, and the best way to reach the entire world is via Android. Of course that excludes the iPhone, but the new Gemini assistant isn’t displacing Siri anytime soon!

That, though, gets why the focus on Android is notable: one possible strategy for Google would have been to make its AI assistant efforts exclusive to Pixel, which The Information reported might happen late last year; the rumored name for the Pixel-exclusive-assistant was “Pixie”. I wrote in Google’s True Moonshot:

What, though, if the mission statement were the moonshot all along? What if “I’m Feeling Lucky” were not a whimsical button on a spartan home page, but the default way of interacting with all of the world’s information? What if an AI Assistant were so good, and so natural, that anyone with seamless access to it simply used it all the time, without thought?

That, needless to say, is probably the only thing that truly scares Apple. Yes, Android has its advantages to iOS, but they aren’t particularly meaningful to most people, and even for those that care — like me — they are not large enough to give up on iOS’s overall superior user experience. The only thing that drives meaningful shifts in platform marketshare are paradigm shifts, and while I doubt the v1 version of Pixie would be good enough to drive switching from iPhone users, there is at least a path to where it does exactly that.

Of course Pixel would need to win in the Android space first, and that would mean massively more investment by Google in go-to-market activities in particular, from opening stores to subsidizing carriers to ramping up production capacity. It would not be cheap, which is why it’s no surprise that Google hasn’t truly invested to make Pixel a meaningful player in the smartphone space.

The potential payoff, though, is astronomical: a world with Pixie everywhere means a world where Google makes real money from selling hardware, in addition to services for enterprises and schools, and cloud services that leverage Google’s infrastructure to provide the same capabilities to businesses. Moreover, it’s a world where Google is truly integrated: the company already makes the chips, in both its phones and its data centers, it makes the models, and it does it all with the largest collection of data in the world.

This path does away with the messiness of complicated relationships with OEMs and developers and the like, which I think suits the company: Google, at its core, has always been much more like Apple than Microsoft. It wants to control everything, it just needs to do it legally; that the best manifestation of AI is almost certainly dependent on a fully integrated (and thus fully seamless) experience means that the company can both control everything and, if it pulls this gambit off, serve everyone.

The problem is that the risks are massive: Google would not only be risking search revenue, it would also estrange its OEM partners, all while spending astronomical amounts of money. The attempt to be the one AI Assistant that everyone uses — and pays for — is the polar opposite of the conservative approach the company has taken to the Google Aggregator Paradox. Paying for defaults and buying off competitors is the strategy of a company seeking to protect what it has; spending on a bold assault on the most dominant company in tech is to risk it all.

I’ve referenced this piece a few times over the last year, including when Osterloh, the founding father of Pixel, took over Android as well. I said in an Update at the time:

Google has a very long ways to go to make [Google’s True Moonshot] a reality, or, frankly, to even make it a corporate goal. It will cost a lot of money, risk partnerships, and lower margins. It is, though, a massive opportunity — the maximal application of AI to Google’s business prospects — and it strikes me as a pretty big deal that, at least when it comes to the org chart, the Pixel has been elevated above Android.

In fact, though, my takeaway from yesterday’s event is the opposite: Android still matters most, and the integration Google is truly betting on is with the cloud.

The Pixel Angle

That’s not to say that Google is giving up on integration completely; President of Android Ecosystem Sameer Samat framed Google’s approach this way:

As Rick said earlier, this is where our decades of investment in AI and Google’s full tech stack make Gemini really special. Gemini can handle these kinds of complex personal queries within Google’s own secure cloud, without sending any of your personal data to a third-party AI provider you may not know or trust. And for some of the most sensitive use cases, like summarizing audio from a phone call, or suggesting a helpful reply to an encrypted text message, we’ve pioneered on-device generative AI with Gemini Nano. It’s the first time a large multimodal AI model has been optimized for a mobile device, so the data never leaves your phone. With Gemini deeply integrated with Android, we’re well on our way to rebuilding the OS with AI at the core. The new Gemini assistant brings the benefits of AI to billions around the world, while helping to keep your personal information secure and private. Android is truly leading the way towards AI for everyone.

To the extent Google is betting on Pixel, it’s that the company will deliver on this hybrid approach more effectively than anyone else in the ecosystem, and that’s not nothing: Google has ultimate control of a Pixel device, and can reap all of the benefits of integration I highlighted above.

In the end, though, Google’s real bet is that owning the information stack matters more than owning the tech stack, particularly when you have the most capable cloud infrastructure to act on it. That is, of course, the same Android strategy as always; the bet is that AI does the hard work of making it more attractive to premium customers than it has to date.

Integration, Innovation, and the DOJ

From Bloomberg:

A bid to break up Alphabet Inc.’s Google is one of the options being considered by the Justice Department after a landmark court ruling found that the company monopolized the online search market, according to people with knowledge of the deliberations.

The move would be Washington’s first push to dismantle a company for illegal monopolization since unsuccessful efforts to break up Microsoft Corp. two decades ago. Less severe options include forcing Google to share more data with competitors and measures to prevent it from gaining an unfair advantage in AI products, said the people, who asked not to be identified discussing private conversations.

Regardless, the government will likely seek a ban on the type of exclusive contracts that were at the center of its case against Google. If the Justice Department pushes ahead with a breakup plan, the most likely units for divestment are the Android operating system and Google’s web browser Chrome, said the people. Officials are also looking at trying to force a possible sale of AdWords, the platform the company uses to sell text advertising, one of the people said.

As I noted last week, I think the remedy that actually addresses the issues in this case is that ban on exclusive contracts.

One important takeaway from yesterday’s Google event, though, and the overall discussion of the importance of integration, is that I think forcing a divesture of Android in particular would be a mistake. Yes, you can envision a world where Android spurs competition amongst AI providers by being open to the highest bidder, or the best product; note, though, that is basically what Apple Intelligence is proffering. Apple is handling AI related to your personal data that is held on the device they sell, and they are really the only ones that can do so; “world knowledge” is being handled by OpenAI for now, but the company has been clear that there will be other offerings.

What Google is proposing is something different entirely: you can pick your device, but your AI will be integrated with your data primarily via the cloud; the company can pull that off because they own both Android and the cloud. It’s something different and, to the extent the DOJ is concerned with historial patterns of innovation, they should let Google’s integration be.

This Article is available as a video essay on YouTube

Railroads were, in theory, an attractive business: while they cost a lot to build, once built, the marginal cost of carrying additional goods was extremely low; sure, you needed to actually run a train, which need fuel and workers and which depreciated the equipment involved, but those costs were minimal compared to the revenue that could be earned from carrying goods for customers that had to pay to gain access to the national market unlocked by said railroads.

The problem for railroad entrepreneurs in the 19th century is that they were legion: as locomotion technology advanced and became standardized, and steel production became a massive industry in its own right, fierce competition arose to tie the sprawling United States together. This was a problem for those seemingly attractive railroad economics: sure, marginal costs were low, but that meant a race to the bottom in terms of pricing (which is based on covering marginal costs, ergo, low marginal costs mean a low floor in terms of the market clearing price). It was the investors in railroads — the ones who paid the upfront cost of building the tracks in the hope of large profits on low marginal cost service, and who were often competing for (and ultimately with) government land grants and subsidies, further fueling the boom — that were left holding the bag.

This story, like so many technological revolutions, culminated in a crash, in this case the Panic of 1873. The triggering factor for the Panic of 1873 was actually currency, as the U.S. responded to a German decision to no longer mint silver coins by changing its policy of backing the U.S. dollar with both silver and gold to gold only, which dramatically tightened the money supply, leading to a steep rise in interest rates. This was a big problem for railway financiers who could no longer service their debts; their bankruptcies led to a slew of failed banks and even the temporary closure of the New York Stock Exchange, which rippled through the economy, leading to a multi-year depression and the failure of over 100 railroads within the following year.

Meanwhile, oil, then used primarily to refine kerosene for lighting, was discovered in Titusville, Pennsylvania in 1859, Bradford, Pennsylvania in 1871, and Lima, Ohio in 1885. The most efficient refineries in the world, thanks to both vertical integration and innovative product creation using waste products from the kerosene refinement process, including the novel use of gasoline as a power source, were run by John D. Rockefeller in Cleveland. Rockefeller’s efficiencies led to a massive boom in demand for kerosene lighting, that Rockefeller was determined to meet; his price advantage — driven by innovation — allowed him to undercut competitors, forcing them to sell to Rockefeller, who would then increase their efficiency, furthering the supply of cheap kerosene, which drove even more demand.

This entire process entailed moving a lot of products around in bulk: oil needed to be shipped to refineries, and kerosene to burgeoning cities through the Midwest and east coast. This was a godsend to the struggling railroad industry: instead of struggling to fill trains from small-scale and sporadic shippers, they signed long-term contracts with Standard Oil; guaranteed oil transportation covered marginal costs, freeing up the railroads to charge higher profit-making rates on those small-scale and sporadic shippers. Those contracts, in turn, gave Standard Oil a durable price advantage in terms of kerosene: having bought up the entire Ohio refining industry through a price advantage earned through innovation and efficiency, Standard Oil was now in a position to do the same to the entire country through a price advantage gained through contracts with railroad companies.

The Sherman Antitrust Act

There were, to be clear, massive consumer benefits to Rockefeller’s actions: Standard Oil, more than any other entity, brought literal light to the masses, even if the masses didn’t fully understand the ways in which they benefited from Rockefeller’s machinations; it was the people who understood the costs — particularly the small businesses and farmers of the Midwest generally, and Ohio in particular — who raised a ruckus. They were the “small-scale and sporadic shippers” I referenced above, and the fact that they had to pay far more for railroad transportation in a Standard Oil world than they had in the previous period of speculation and over-investment caught the attention of politicians, particularly Senator John Sherman of Ohio.

Senator Sherman had not previously shown a huge amount of interest in the issue of monopoly and trusts, but he did have oft-defeated presidential aspirations, and seized on the discontent with Standard Oil and the railroads to revive a bill originally authored by a Vermont Senator named George Edmunds; the relevant sections of the Sherman Antitrust Act were short and sweet and targeted squarely at Standard Oil’s contractual machinations:

Sec. 1. Every contract, combination in the form of trust or otherwise, or conspiracy, in restraint of trade or commerce among the several States, or with foreign nations, is hereby declared to be illegal. Every person who shall make any such contract or engage in any such combination or conspiracy, shall be deemed guilty of a misdemeanor, and, on conviction thereof, shall be punished by fine not exceeding five thousand dollars, or by imprisonment not exceeding one year, or by both said punishments, at the discretion of the court.

Sec. 2. Every person who shall monopolize, or attempt to monopolize, or combine or conspire with any other person or persons, to monopolize any part of the trade or commerce among the several States, or with foreign nations, shall be deemed guilty of a misdemeanor, and, on conviction thereof; shall be punished by fine not exceeding five thousand dollars, or by imprisonment not exceeding one year, or by both said punishments, in the discretion of the court.

And so we arrive at Google.

The Google Case

Yesterday, from the Wall Street Journal:

A federal judge ruled that Google engaged in illegal practices to preserve its search engine monopoly, delivering a major antitrust victory to the Justice Department in its effort to rein in Silicon Valley technology giants. Google, which performs about 90% of the world’s internet searches, exploited its market dominance to stomp out competitors, U.S. District Judge Amit P. Mehta in Washington, D.C. said in the long-awaited ruling.

“Google is a monopolist, and it has acted as one to maintain its monopoly,” Mehta wrote in his 276-page decision released Monday, in which he also faulted the company for destroying internal messages that could have been useful in the case. Mehta agreed with the central argument made by the Justice Department and 38 states and territories that Google suppressed competition by paying billions of dollars to operators of web browsers and phone manufacturers to be their default search engine. That allowed the company to maintain a dominant position in the sponsored text advertising that accompanies search results, Mehta said.

While there have been a number of antitrust laws passed by Congress, most notably the Clayton Antitrust Act of 1914 and Federal Trade Commission Act of 1914, the Google case is directly downstream of the Sherman Act, specifically Section 2, and its associated jurisprudence. Judge Mehta wrote in his 286-page opinion:

“Section 2 of the Sherman Act makes it unlawful for a firm to ‘monopolize.’” United States v. Microsoft, 253 F.3d 34, 50 (D.C. Cir. 2001) (citing 15 U.S.C. § 2). The offense of monopolization requires proof of two elements: “(1) the possession of monopoly power in the relevant market and (2) the willful acquisition or maintenance of that power as distinguished from growth or development as a consequence of a superior product, business acumen, or historic accident.” United States v. Grinnell Corp., 384 U.S. 563, 570–71 (1966).

Note that Microsoft reference: the 1990s antitrust case provides the analytical framework Mehta used in this case.

The court structures its conclusions of law consistent with _Microsoft_’s analytical framework. After first summarizing the principles governing market definition, infra Section II.A, the court in Section II.B addresses whether general search services is a relevant product market, and finding that it is, then evaluates in Section II.C whether Google has monopoly power in that market. In Part III, the court considers the three proposed advertiser-side markets. The court finds that Plaintiffs have established two relevant markets — search advertising and general search text advertising — but that Google possesses monopoly power only in the narrower market for general search text advertising. All parties agree that the relevant geographic market is the United States.

The court then determines whether Google has engaged in exclusionary conduct in the relevant product markets. Plaintiffs’ primary theory centers on Google’s distribution agreements with browser developers, OEMs, and carriers. The court first addresses in Part IV whether the distribution agreements are exclusive under Microsoft. Finding that they are, the court then analyzes in Parts V and VI whether the contracts have anticompetitive effects and procompetitive justifications in each market. For reasons that will become evident, the court does not reach the balancing of anticompetitive effects and procompetitive justifications. Ultimately, the court concludes that Google’s exclusive distribution agreements have contributed to Google’s maintenance of its monopoly power in two relevant markets: general search services and general search text advertising.

I find Mehta’s opinion well-written and exhaustive, but the decision is ultimately as simple as the Sherman Act: Google acquired a monopoly in search through innovation, but having achieved a monopoly, it is forbidden from extending that monopoly through the use of contractual arrangements like the default search deals it has with browser developers, device makers, and carriers. That’s it!

Aggregators and Contracts

To me this simplicity is the key to the case, and why I argued from the get-go that the Department of Justice was taking a far more rational approach to prosecuting a big tech monopoly than the FTC or European Commission had been. From a 2020 Stratechery Article entitled United States v. Google:

The problem with the vast majority of antitrust complaints about big tech generally, and online services specifically, is that Page is right [about competition only being a click away]. You may only have one choice of cable company or phone service or any number of physical goods and real-world services, but on the Internet everything is just zero marginal bits.

That, though, means there is an abundance of data, and Google helps consumers manage that abundance better than anyone. This, in turn, leads Google’s suppliers to work to make Google better — what is SEO but a collective effort by basically the entire Internet to ensure that Google’s search engine is as good as possible? — which attracts more consumers, which drives suppliers to work even harder in a virtuous cycle. Meanwhile, Google is collecting information from all of those consumers, particularly what results they click on for which searches, to continuously hone its accuracy and relevance, making the product that much better, attracting that many more end users, in another virtuous cycle:

One of the central ongoing projects of this site has been to argue that this phenomenon, which I call Aggregation Theory, is endemic to digital markets…In short, increased digitization leads to increased centralization (the opposite of what many originally assumed about the Internet). It also provides a lot of consumer benefit — again, Aggregators win by building ever better products for consumers — which is why Aggregators are broadly popular in a way that traditional monopolists are not…

The solution, to be clear, is not simply throwing one’s hands up in the air and despairing that nothing can be done. It is nearly impossible to break up an Aggregator’s virtuous cycle once it is spinning, both because there isn’t a good legal case to do so (again, consumers are benefitting!), and because the cycle itself is so strong. What regulators can do, though, is prevent Aggregators from artificially enhancing their natural advantages…

That is exactly what this case was about:

This is exactly why I am so pleased to see how narrowly focused the Justice Department’s lawsuit is: instead of trying to argue that Google should not make search results better, the Justice Department is arguing that Google, given its inherent advantages as a monopoly, should have to win on the merits of its product, not the inevitably larger size of its revenue share agreements. In other words, Google can enjoy the natural fruits of being an Aggregator, it just can’t use artificial means — in this case contracts — to extend that inherent advantage.

I laid out these principles in 2019’s A Framework for Regulating Competition on the Internet, and it was this framework that led me to support the DOJ’s case initially, and applaud Judge Mehta’s decision today.

Mehta’s decision, though, is only about liability: now comes the question of remedies, and the truly difficult questions for me and my frameworks.

Friendly Google

The reason to start this Article with railroads and Rockefeller and the history of the Sherman Antitrust Act is not simply to provide context for this case; rather, it’s important to understand that antitrust is inherently political, which is another way of saying it’s not some sort of morality play with clearly distinguishable heroes and villains. In the case of Standard Oil, the ultimate dispute was between the small business owners and farmers of the Midwest and city dwellers who could light their homes thanks to cheap kerosene. To assume that Rockefeller was nothing but a villain is to deny the ways in which his drive for efficiency created entirely new markets that resulted in large amounts of consumer welfare; moreover, there is an argument that Standard Oil actually benefited its political enemies as well, by stabilizing and standardizing the railroad industry that they ultimately resented paying for.

Indeed, there are some who argue, even today, that all of antitrust law is misguided, because like all centralized interference in markets, it fails to properly balance the costs and benefits of interference with those markets. To go back to the Standard Oil example, those who benefited from cheap kerosene were not politically motivated to defend Rockefeller, but their welfare was in fact properly weighed by the market forces that resulted in Rockefeller’s dominance. Ultimately, though, this is a theoretical argument, because politics do matter, and Sherman tapped into a deep and longstanding discomfort in American society with dominant entities like Standard Oil then, and Google today; that’s why the Sherman Antitrust Act passed by a vote of 51-1 in the Senate, and 242-0 in the House.

Then something funny happened: Standard Oil was indeed prosecuted under the Sherman Antitrust Act, and ordered to be broken up into 34 distinct companies; Rockefeller had long since retired from active management at that point, but still owned 25% of the company, and thus 25% of the post-breakup companies. Those companies, once listed, ended up being worth double what they were as Standard Oil; Rockefeller ended up richer than ever. Moreover, it was those companies, like Exxon, that ended up driving a massive increase in oil by expanding from refining to exploration all over the world.

The drivers of that paradox are evident in the consideration of remedies for Google. One possibility is a European Commission-style search engine choice screen for consumers setting up new browsers or devices: is there any doubt that the vast majority of people will choose Google, meaning Google keeps its share and gets to keep the money it gives Apple and everyone else? Another is that Google is barred from bidding for default placement, but other search engines can: that will put entities like Apple in the uncomfortable position of either setting what it considers the best search engine as the default, and making no money for doing so, or prioritizing a revenue share from an also-ran like Bing — and potentially seeing customers go to Google anyways. The iPhone maker could even go so far as to build its own search engine, and seek to profit directly from the search results driven by its distribution advantage, but that entails tremendous risk and expense on the part of the iPhone maker, and potentially favors Android.

That, though, was the point: the cleverness of Google’s strategy was their focus on making friends instead of enemies, thus motivating Apple in particular to not even try. I told Michael Nathanson and Craig Moffett when asked in a recent interview why Apple doesn’t build a search engine:

Apple already has a partnership with Google, the Google-Apple partnership is really just a beautiful manifestation of how, don’t-call-them-monopolies, can really scratch each other’s back in a favorable way, such that Google search makes up something like 17% or 20% of Apple’s profit, it’s basically pure profit for Apple and people always talk about, “When’s Apple going to make a search engine?” — the answer is never. Why would they? They get the best search engine, they get profits from that search engine without having to drop a dime of investment, they get to maintain their privacy brand and say bad things about data-driven advertising, while basically getting paid by what they claim to hate, because Google is just laundering it for them. Google meanwhile gets the scale, there’s no points of entry for potential competition, it makes total sense.

This wasn’t always Google’s approach; in the early years of the smartphone era the company had designs on Android surpassing the iPhone, and it was a whole-company effort. That, mistakenly in my view, at least from a business perspective, meant using Google’s services — specifically Google Maps — to differentiate Android, including shipping turn-by-turn directions on Android only, and demanding huge amounts of user data from Apple to maintain an inferior product for the iPhone.

Apple’s response was shocking at the time: the company would build its own Maps product, even though that meant short term embarrassment. It was also effective, as evidenced by testimony in this case. From Bloomberg last fall:

Two years after Apple Inc. dropped Google Maps as its default service on iPhones in favor of its own app, Google had regained only 40% of the mobile traffic it used to have on its mapping service, a Google executive testified in the antitrust trial against the Alphabet Inc. company. Michael Roszak, Google’s vice president for finance, said Tuesday that the company used the Apple Maps switch as “a data point” when modeling what might happen if the iPhone maker replaced Google’s search engine as the default on Apple’s Safari browser.

The lesson Google learned was that Apple’s distribution advantages mattered a lot, which by extension meant it was better to be Apple’s friend than its enemy. I wrote in an Update after that revelation:

This does raise a question I get frequently: how can I argue that Google wins by being better when it is willing to pay for default status? I articulated the answer on a recent episode of Sharp Tech, but briefly, nothing exists in a vacuum: defaults do matter, and that absolutely impacts how much better you have to be to force a switch. In this case Google took the possibility off of the table completely, and it was a pretty rational decision in my mind.

It also, without question, reduced competition in the space, which is why I always thought this was a case worth taking to court. This is in fact a case where I think even a loss is worthwhile, because I find contracts between Aggregators to be particularly problematic. Ultimately, though, my objection to this arrangement is just as much, if not more, about Apple and its power. They are the ones with the power to set the defaults, and they are the ones taking the money instead of competing; it’s hard to fault Google for being willing to pay up.

Tech companies, particularly advertising-based ones, famously generate huge amounts of consumer surplus. Yes, Google makes a lot of money showing you ads, but even at a $300+ billion run rate, the company is surely generating far more value for consumers than it is capturing. That is in itself some degree of defense for the company, I should note, much as Standard Oil brought light to every level of society; what is notable about these contractual agreements, though, is how Google has been generating surplus for everyone else in the tech industry.

Maybe this is a good thing; it’s certainly good for Mozilla, which gets around 80% of its revenue from its Google deal. It has been good for device makers, commoditized by Android, who have an opportunity for scraps of profit. It has certainly been profitable for Apple, which has seen its high-margin services revenue skyrocket, thanks in part to the ~$20 billion per year of pure profit it gets from Google without needing to make any level of commensurate investment.

Enemy Remedies

However, has it been good for Google, not just in terms of the traffic acquisition costs it pays out, but also in terms of the company’s maintenance of the drive that gave it its dominant position in the first place? It’s a lot easier to pay off your would-be competitors than it is to innovate. I’m hesitant to say that antitrust is good for its subjects, but Google does make you wonder.

Most importantly, has it been good for consumers? This is where the Apple Maps example looms large: Apple has shown it can compete with Google if it puts resources behind a project it considers core to the iPhone experience. By extension, the entire reason why Google favored Google Maps in the first place, leaving Apple no choice but to compete, is because they were seeking to advantage Android relative to the iPhone. Both competitions drove large amounts of consumer benefit that continue to persist today.

I would also note that the behavior I am calling for — more innovation and competition, not just from Google’s competitors, but Google itself — is the exact opposite of what the European Union is pushing for, which is product stasis. I think the E.U. is mistaken for the exact same reasons I think Judge Mehta is right.

There’s also the political point: I am an American, and I share the societal sense of discomfort in dominant entities that made the Sherman Antitrust Act law in the first place; yes, it’s possible this decision doesn’t mean much in the end, but it’s pushing in a direction that is worth leaning into

This is why, ultimately, I am comfortable with the implications of my framework, and why I think the answer to the remedy question is an injunction against Google making any sort of payments or revenue share for search; if you’re a monopoly you don’t get to extend your advantage with contracts, period (now do most-favored nation clauses). More broadly, we tend to think of monopolies as being mean; the problem with Aggregators is they have the temptation to be too nice. It has been very profitable to be Google’s friend; I think consumers — and Google — are better off if the company has a few more enemies.

I wrote a follow-up to this Article in this Daily Update.

This Article is available as a video essay on YouTube

I’ve long maintained that if the powers-that-be understood what the Internet’s impact would be, they would have never allowed it to be created. It’s hard to accuse said shadowy figures of negligence, however, given how clueless technologists were as well; look no further than an operating system like Windows.

Windows was, from the beginning, well and truly open: 3rd-party developers could do anything, including “patching the kernel”; to briefly summarize:

• The “kernel” of an operating system is the core of the operating system, the function of which is to manage the actual hardware of a computer. All software running in the kernel is fully privileged, which is to say it operates without any restrictions. If software crashes in the kernel, the entire computer crashes.
• Everything else on a computer runs in “user space”; user space effectively sits on top of the kernel, and is dependent on APIs provided by the operating system maker to compel software in kernel space to actually interface with the hardware. If software crashes in user space the rest of the computer is fine.

This is a drastically simplified explanation; in some operating systems there are levels of access between kernel space and user space for things like drivers (which need direct access to the hardware they are controlling, but not necessarily hardware access to the entire computer), and on the other side of things significant limitations on software in user space (apps, for example, might be “sandboxed” and unable to access other information on the computer, even if it is in user space).

The key point for purposes of this Article, though, is that Windows allowed access to both kernel space and user space; yes, the company certainly preferred that developers only operate in user space, and the company never officially supported applications that patched the kernel, but the reality is that operating in kernel space is far more powerful and so a lot of developers would do just that.

Security Companies and Kernel Access

An example of developers with a legitimate argument for access to kernel space are security companies: Windows’ openness extended beyond easy access to kernel space; the reason why sandboxing became a core security feature of newer operating systems like iOS is that not all developers were good actors: virus and malware makers on Windows in particular would leverage easy access to other programs to infiltrate computers and make them nigh on unusable at best, and exfiltrate data or use computers they took over to attack others at worse.

The goal of security software like antivirus programs or malware scanners was to catch these bad actors and eliminate them; the best way to do so was to patch the kernel and so operate at the lowest, most powerful layer of Windows, with full visibility and access to every other program running on the computer. And, to be clear, in the 2000s, when viruses and malware were at their peak, this was very much necessary — and necessary is another way of saying this was a clear business opportunity.

Two of the companies seizing this opportunity in the 2000s were Symantec and McAfee; both reacted with outrage in 2005 and 2006 when Microsoft, in the run-up to the release of Windows Vista, introduced PatchGuard. PatchGuard was aptly named: it guarded the kernel from being patched by 3rd-parties, with the goal of increasing security. This, though, was a threat to Symantec and McAfee; George Amenuk, CEO of the latter, released an open letter that stated:

Over the years, the most reliable defenders against the many, many vulnerabilities in the Microsoft operating systems have been the independent security companies such as McAfee. Yet, if Microsoft succeeds in its latest effort to hamstring these competitors, computers everywhere could be less secure. Computers are more secure today, thanks to relentless innovations by the security providers. Microsoft also has helped by allowing these companies’ products full access to system resources-this has enabled the security products to better “see” threats and deploy defenses against viruses and other attacks.

With its upcoming Vista operating system, Microsoft is embracing the flawed logic that computers will be more secure if it stops cooperating with the independent security firms. For the first time, Microsoft shut off security providers’ access to the core of its operating system – what is known as the “kernel”.

At the same time, Microsoft has firmly embedded in Vista its own Windows Security Center-a product that cannot be disabled even when the user purchases an alternative security solution. This approach results in confusion for customers and prevents genuine freedom of choice. Microsoft seems to envision a world in which one giant company not only controls the systems that drive most computers around the world but also the security that protects those computers from viruses and other online threats. Only one approach protecting us all: when it fails, it fails for 97% of the world’s desktops.

Symantec, meanwhile, went straight to E.U. regulators, making the case that Microsoft, already in trouble over its inclusion of Internet Explorer in the 90s, and Windows Media Player in the early 2000s, was unfairly limiting competition for security offerings. The E.U. agreed and Microsoft soon backed down; from Silicon.com in 2006:

Microsoft has announced it will give security software makers technology to access the kernel of 64-bit versions of Vista for security-monitoring purposes. But its security rivals remain as yet unconvinced. Redmond also said it will make it possible for security companies to disable certain parts of the Windows Security Center in Vista when a third-party security console is installed. Microsoft made both changes in response to antitrust concerns from the European Commission. Led by Symantec, the world’s largest antivirus software maker, security companies had publicly criticised Microsoft over both Vista features and also talked to European competition officials about their gripes.

Fast forward nearly two decades, and while Symantec and McAfee are still around, there is a new wave of cloud-based security companies that dominate the space, including CrowdStrike; Windows is much more secure than it used to be, but after the disastrous 2000s, a wave of regulations were imposed on companies requiring them to adhere to a host of requirements that are best met by subscribing to an all-in-one solution that checks all of the relevant boxes, and CrowdStrike fits the bill. What is the same is kernel-level access, and that brings us to last week’s disaster.

The CrowdStrike Crash

On Friday, from The Verge:

Thousands of Windows machines are experiencing a Blue Screen of Death (BSOD) issue at boot today, impacting banks, airlines, TV broadcasters, supermarkets, and many more businesses worldwide. A faulty update from cybersecurity provider CrowdStrike is knocking affected PCs and servers offline, forcing them into a recovery boot loop so machines can’t start properly. The issue is not being caused by Microsoft but by third-party CrowdStrike software that’s widely used by many businesses worldwide for managing the security of Windows PCs and servers.

On Saturday, from the CrowdStrike blog:

On July 19, 2024 at 04:09 UTC, as part of ongoing operations, CrowdStrike released a sensor configuration update to Windows systems. Sensor configuration updates are an ongoing part of the protection mechanisms of the Falcon platform. This configuration update triggered a logic error resulting in a system crash and blue screen (BSOD) on impacted systems. The sensor configuration update that caused the system crash was remediated on Friday, July 19, 2024 05:27 UTC. This issue is not the result of or related to a cyberattack.

In any massive failure there are a host of smaller errors that compound; in this case, CrowdStrike created a faulty file, failed to test it properly, and deployed it to its entire customer base in one shot, instead of rolling it out in batches. Doing something different at each one of these steps would have prevented the widespread failures that are still roiling the world (and will for some time to come, given that the fix requires individual action on every affected computer, since the computer can’t stay running long enough to run a remotely delivered fix).

The real issue, though, is more fundamental: erroneous configuration files in userspace crash a program, but they don’t crash the computer; CrowdStrike, though, doesn’t run in userspace: it runs in kernel space, which means its bugs crash the entire computer — 8 million of them, according to Microsoft. Apple and Linux were not impacted, for a very obvious reason: both have long since locked out 3rd-party software from kernel space.

Microsoft, though, despite having tried to do just that in the 2000s, has its hands tied; from the Wall Street Journal:

A Microsoft spokesman said it cannot legally wall off its operating system in the same way Apple does because of an understanding it reached with the European Commission following a complaint. In 2009, Microsoft agreed it would give makers of security software the same level of access to Windows that Microsoft gets.

I wasn’t able to find the specifics around the agreement Microsoft made with the European Commission; the company did agree to implement a browser choice screen in December 2009, along with a commitment to interoperability for its “high-share software products” including Windows. What I do know is that a complaint about kernel level access was filed by Symantec, that Microsoft was under widespread antitrust pressure by regulators, and, well, that a mistake by CrowdStrike rendered millions of computers inoperable because CrowdStrike has kernel access.

Microsoft’s Handicap

On Friday afternoon, FTC Chair Lina Khan tweeted:

1. All too often these days, a single glitch results in a system-wide outage, affecting industries from healthcare and airlines to banks and auto-dealers. Millions of people and businesses pay the price.

These incidents reveal how concentration can create fragile systems.

— Lina Khan (@linakhanFTC) July 19, 2024

This is wrong on a couple of levels, but the ways in which it is wrong are worth examining because of what they mean for security specifically and tech regulation broadly.

First, this outage was the system working as regulators intended: 99% of Windows computers were not affected, just those secured by CrowdStrike; to go back to that 2006 open letter from the McAfee CEO:

We think customers large and small are right to rely on the innovation arising from the intense competition between diverse and independent security companies. Companies like McAfee have none of the conflicts of interest deriving from ownership of the operating system. We focus purely on security. Independent security developers have proven to be the most powerful weapon in the struggle against those who prey on weak computers. Computer users around the globe recognize that the most serious threats to security exist because of inherent weaknesses in the Microsoft operating system. We believe they should demand better of Microsoft.

For starters, customers should recognize that Microsoft is being completely unrealistic if, by locking security companies out of the kernel, it thinks hackers won’t crack Vista’s kernel. In fact, they already have. What’s more, few threats actually target the kernel – they target programs or applications. Yet the unfettered access previously enjoyed by security providers has been a key part of keeping those programs and applications safe from hackers and malicious software. Total access for developers has meant better protection for customers.

That argument may be correct; the question this episode raises, though, is what is the appropriate level of abstraction to evaluate risk? The McAfee CEO’s argument is that most threats are targeting userspace, which is why security developers deserve access to kernel space to root them out; again, I think this argument is probably correct in a narrow sense — it was definitely correct in the malware-infested 2000s — but what is a bigger systemic problem, malware and viruses on functioning computers, or computers that can’t even turn on?

Second, while Khan’s tweets didn’t mention Microsoft specifically, it seems obvious that is the company she was referring to; after all, CrowdStrike, who was actually to blame, is apparently only on 1% of Windows PCs, which even by the FTC’s standards surely doesn’t count as “concentration.” In this Khan was hardly alone: the company that is taking the biggest public relations hit is Microsoft, and how could they not:

Everyone around the world encountered these images everywhere, both in person and on social media:

This tweet was a joke, but from Microsoft’s position, apt: if prison is the restriction of freedom by the authorities, well, then that is exactly how this happened, as regulators restricted Microsoft’s long-sought freedom to lock down kernel space.

To be clear, restricting access to kernel space would not have made an issue like this impossible: after all, Microsoft, by definition, will always have access to kernel space, and they could very well issue an update that crashes not just 1% of the world’s Windows computers, but all of them. This, though, raises the question of incentives: is there any company both more motivated and better equipped than Microsoft to not make this sort of mistake, given the price they are paying today for a mistake that wasn’t even their fault?

Regulating Progress

Cloudflare CEO Matthew Prince already anticipated the potential solution I am driving at, and wrote a retort on X:

Here’s the scary thing that’s likely to happen based on the facts of the day if we don’t pay attention. Microsoft, who competes with @CrowdStrike, will argue that they should lock all third-party security vendors out of their OS. “It’s the only way we can be safe,” they’ll testify before Congress.

But lest we forget, Microsoft themselves had their own eternal screw up where they potentially let a foreign actor read every customer’s email because they failed to adequately secure their session signing keys. We still have no idea how bad the implications of #EternalBlue are.

So pick your poison. Today CrowdStrike messed up and some systems got locked out. That sucks a measurable amount. On the other hand, if Microsoft runs the app and security then they mess up and you’ll probably still be able to check your email — because their incentive is to fail open — but you’ll never know who else could too. Not to mention your docs, apps, files, and everything else.

Today sucked, but better security isn’t consolidated security. It isn’t your application provider picking who your security vendor must be. It’s open competition across many providers. Because CrowdStrike had a bad day, but the solution isn’t to standardize on Microsoft.

And, if we do, then when they have a bad day it’ll make today look like a walk in the park.

Prince’s argument is ultimately an updated version of that made by the McAfee CEO, and while I agree in theory, in this specific instance I disagree in practice: Windows gave kernel access because the company didn’t know any better, but just because the company won in its market doesn’t mean decisions made decades ago must then be the norm forever.

This is a mistake that I think that regulators make regularly, particularly in Europe. Last week I wrote in the context of the European Commission’s investigation of X and blue checkmarks:

One of the critiques of European economies is how difficult it is to fire people; while the first-order intentions are obviously understandable, the critique is that companies underinvest in growth because there is so much risk attached to hiring: if you get the wrong person, or if expected growth doesn’t materialize, you are stuck. What is notable is how Europe seems to have decided on the same approach to product development: Google is expected to have 10 blue links forever, Microsoft can’t include a browser or shift the center of gravity of its business to Teams, Apple can’t use user data for Apple Intelligence, and, in this case, X is forever bound to the European Commission’s interpretation of what a blue check meant under previous ownership. Everything, once successful, must be forever frozen in time; ultimately, though, the E.U. only governs a portion of Europe, and the only ones stuck in the rapidly receding past — for better or worse! — will be the E.U.’s own citizens.

In this case, people all over the world suffered because Microsoft was never allowed to implement a shift in security that it knew was necessary two decades ago.

More broadly, regulators need to understand that everything is a trade-off. Apple is under fire for its App Store policies — which I too have been relentlessly critical of — but as I wrote in The E.U. Goes Too Far earlier this month:

Apple didn’t just create the iPhone, they also created the App Store, which, after the malware and virus muddled mess of the 2000s, rebuilt user confidence and willingness to download 3rd-party apps. This was a massive boon to developers, and shouldn’t be forgotten; more broadly, the App Store specifically and Apple’s iOS security model generally really do address real threats that can not only hurt users but, by extension, chill the market for 3rd-party developers.

I went on to explain how Apple has gone too far with this model, particularly with its policy choices in the App Store that seem to be motivated more by protecting App Store revenue than security (and why the European Commission was right to go after anti-steering policies in particular), but I included the excerpted paragraph as a reminder that these are hard questions.

What does seem clear to me is that the way to answer hard questions is to not seek to freeze technology in time but rather to consider how many regulatory obsessions — including Windows dominance — are ultimately addressed by technology getting better, not by regulators treating mistaken assumptions (like operating system openness being an unalloyed good) as unchangeable grounds for competition.

This Article is available as a video essay on YouTube

Apple’s increasingly fraught adventures with European regulators is a bit of a morality tale, which goes something like this:

The erstwhile underdog, arguably kept alive by its biggest rival to maintain a figment of competition in the PC market, rides unexpected success in music players to create the iPhone, the device that changed the world while making Apple one of the richest companies in history. Apple, though, seemingly unaware of its relative change in status and power, holds to its pattern — decried in that also-ran PC-era as the cause of their struggles — of total control, not just of the operating system its devices run, but also of 3rd-party applications that helped make the iPhone a behemoth. Ultimately, because of its refusal to compromise, regulators stepped in to force open the iPhone platform, endangering Apple’s core differentiation — the integration of hardware and software — along the way. If only Apple could have seen how the world — and its place in it! — had changed.

This morality tale is one I have chronicled, warned about, and perhaps even helped author over the last decade; like many morality tales, it is mostly true, but also like many morality tales, reality is a bit more nuanced, and filled with alternative morality tales that argue for different conclusions.

Europe’s Data Obsession

During the recent Stratechery break I was in Europe, and, as usual, was pretty annoyed by the terrible Internet experience endemic to the continent: every website has a bunch of regulatory-required pop-ups asking for permission to simply operate as normal websites, which means collecting the data necessary to provide whatever experience you are trying to access. This obviously isn’t a new complaint — I feel the same annoyance every time I visit.

What was different this time is that, for the first time in a while, I was traveling as a tourist with my family, and thus visiting things like museums, making restaurant reservations, etc.; what stood out to me was just how much information all of these entities wanted: seemingly every entity required me to make an account, share my mailing address, often my passport information, etc., just to buy a ticket or secure a table. It felt bizarrely old-fashioned, as if services like OpenTable or Resy didn’t exist, or even niceties like “Sign In With Google”; what exactly is a museum or individual restaurant going to do with so much of my personal information? I just want to see a famous painting or grab a meal!

Your first thought — and mine as well — might be that this is why all of those pop-ups exist: random entities are asking for a lot of my personal data, and I ought to have control of that. I certainly agree with the sentiment — if I lived in Europe and were assaulted with data requests from random entities with such frequency, I would feel similarly motivated — but obviously the implementation is completely broken: hardly anyone, no matter how disciplined about their data, has the time and motivation to read through every privacy policy or data declaration and jump through the hoops necessary to buy a ticket or reserve a table while figuring out the precise set of options necessary to do so without losing control of said data; you just hit “Accept” and buy the damn ticket.

My second thought — and this is certainly influenced by my more business-oriented trips to the continent — is that Europeans sure are obsessed with data generally. On another trip, I was in a forum about AI and was struck by the extent to which European business-people themselves were focused on data to the point where it seemed likely some number of their companies would miss out on potential productivity gains for fear of losing control of what they viewed as some sort of gold mine; the reality is that data is not the new oil: yes, it is valuable at scale and when processed in a data factory, but the entities capable of building such factories are on the scale of a Meta or a Google, not a museum or a restaurant or even a regional bank. I don’t think that AI has changed this equation: the goal of a business ought to be to leverage its data to deliver better business outcomes — which AI should make easier — not obsessively collect and hoard data as if it were a differentiator in its own right.

The third takeaway, though, is the most profound: the Internet experience in America is better because the market was allowed to work. Instead of a regulator mandating sites show pop-ups to provide some sort of false assurance about excess data collection, the vast majority of sites have long since figured out that (1) most of the data they might collect isn’t really that usable, particularly in light of the security risks in holding it, and (2) using third-party services is better both for the customer and themselves. Do you want a reservation? Just click a button or two, using the same service you use everywhere else; want to buy tickets? Just have a credit card, or even better, Apple Pay or Google Wallet.

Moreover, this realization extends to the data obsessives’ bugaboo, advertising. Yes, Internet advertising was a data disaster 15 years ago (i.e. the era where the European Internet seems stuck); the entities that did more than anyone to clean the situation up were in fact Meta and Google: sites and apps realized they could get better business results and reduce risk by essentially outsourcing all data collection and targeting to Meta and Google and completely cut out the ecosystem of data brokers and unaccountable middlemen that defined the first 15 years of the Internet.

Google Shopping

It would be fine — annoying, to be sure, but ultimately fine — if this were where the story ended: the E.U. identifies an issue (excessive data collection) and reaches for a regulatory solution, locking in a terrible status quo, while the U.S.’s more market-oriented approach results in a better experience for users and better business outcomes for businesses. The E.U. is gonna E.U., amirite? After all, this is the regulatory body that somehow thought a browser choice screen would fundamentally alter the balance of power in technology (and to the extent that that old Microsoft decree did, it was to aid Google in locking in Chrome’s dominance).

The first hints that there may be more nefarious motivations to E.U regulation, though, came in a 2017 European Commission decision about Google Shopping, which I wrote about in Ends, Means, and Antitrust. The title referenced the high level takeaway that while I understood the motivation and arguable necessity of regulating Aggregators (the ends), getting the details right mattered as well (the means), and I thought that case fell short for three reasons:

• First, the Google Shopping decision assumed that Google ought never improve the user experience of Google search. Specifically, just because a search for a specific item once returned a shopping comparison site, it was hardly a crime that search improved such that it surfaced the specific item instead of a middleman. I argued that this was bad for users.
• Second, the Google Shopping decision took an artificially narrow view of competition: those random shopping comparison sites that effectively arbitraged insufficiently precise search results in the 1990s were not the sort of competitors that actually delivered the market outcomes that regulation should try to enable; Google’s real competitor in shopping was Amazon and other similarly integrated retailers who were taking users away from search entirely.
• Third, and most problematically, the Google Shopping product in question was actually an advertising unit: the European Commission was getting into the business of dictating how companies could or could not make money.

I wrote in that Article:

You can certainly argue that the tiny “Sponsored” label is bordering on dishonesty, but the fact remains that Google is being explicit about the fact that Google Shopping is a glorified ad unit. Does the European Commission honestly have a problem with that? The entire point of search advertising is to have the opportunity to put a result that might not otherwise rank in front of a user who has demonstrated intent.

The implications of saying this is monopolistic behavior goes to the very heart of Google’s business model: should Google not be allowed to sell advertising against search results for fear that it is ruining competition? Take travel sites: why shouldn’t Priceline sue Google for featuring ads for hotel booking sites above its own results? Why should Google be able to make any money at all?

This is the aspect of the European Commission’s decision that I have the biggest problem with. I agree that Google has a monopoly in search, but as the Commission itself notes that is not a crime; the reality of this ruling, though, is that making any money off that monopoly apparently is. And, by extension, those that blindly support this decision are agreeing that products that succeed by being better for users ought not be able to make money.

Again, there is plenty of room to disagree about what regulations are and are not appropriate, or debate what is the best way to spur competition; the reason I reacted so negatively to this decision, though, was because this specific point struck me as being fundamentally anti-free market: Google was obligated to deliver a search experience on the European Commission’s terms or else. It was a bit of a subtle point, to be sure — the stronger argument was about the validity of product evolution in a way that makes for a better user experience — but recent events suggest I was right to be concerned.

Apple and the Core Technology Fee

Every year June provides a flurry of antitrust news — I guess I am not the only one that goes on vacation in July — and this year was no exception. The most unsurprising was about Apple; from a European Commission press release:

Today, the European Commission has informed Apple of its preliminary view that its App Store rules are in breach of the Digital Markets Act (DMA), as they prevent app developers from freely steering consumers to alternative channels for offers and content. In addition, the Commission opened a new non-compliance procedure against Apple over concerns that its new contractual requirements for third-party app developers and app stores, including Apple’s new “Core Technology Fee”, fall short of ensuring effective compliance with Apple’s obligations under the DMA.

The first item, about Apple’s anti-steering provisions, fits perfectly into the morality tale I opened this Article with. Apple didn’t just create the iPhone, they also created the App Store, which, after the malware and virus muddled mess of the 2000s, rebuilt user confidence and willingness to download 3rd-party apps. This was a massive boon to developers, and shouldn’t be forgotten; more broadly, the App Store specifically and Apple’s iOS security model generally do address real threats that can not only hurt users but, by extension, chill the market for 3rd-party developers.

At the same time, the implications of the App Store model and iOS’s locked-down nature mean that Apple’s power over the app ecosystem is absolute; this not only means that the company can extract whatever fees it likes from developers, it also hinders the development of apps and especially business models that don’t slot in to Apple’s rules. I think it would have been prudent of the company to provide more of a release valve than web apps on Safari: I have long advocated that the company allow non-gaming apps to have webviews that provide alternative payment options of the developers’ choice; Apple instead went the other way, arguing ever more strenuously that developers can’t even talk about or link to their own websites if that website provided alternative payment methods, and to the extent the company gave ground, it was in the most begrudging and clunky way possible.

These anti-steering rules are the first part of the European Commission’s case, and while I might quibble with some of the particulars, I mostly blame Apple for not self-regulating in this regard. Note, though, that the anti-steering case isn’t just about allowing links or pricing information; this is the third charge:

Whilst Apple can receive a fee for facilitating via the AppStore [sic] the initial acquisition of a new customer by developers, the fees charged by Apple go beyond what is strictly necessary for such remuneration. For example, Apple charges developers a fee for every purchase of digital goods or services a user makes within seven days after a link-out from the app.

As I explained in an Update last month, the charges the European Commission seems to be referring to are Apple’s new Core Technology Fee for apps delivered outside of the App Store, a capability which is required by the DMA. Apple’s longstanding argument is that the fees it charges in the App Store are — beyond the 3% that goes to payment providers — compensation for the intellectual property leveraged by developers to make apps; if it can’t collect those fees via a commission on purchases then the company plans to charge developers €0.50 per app install per year.

Now this is where the discussion gets a bit messy and uncomfortable. On one hand, I think that Apple’s policies — and, more irritatingly, its rhetoric — come across as arrogant and unfairly dismissive of the massive contribution that 3rd-party developers have made to the iPhone in particular; there’s a reason why Apple’s early iPhone marketing emphasized that There’s an App for That:

At the same time, to quote an Update I wrote about the Core Technology Fee:

There simply is no question that iOS is Apple’s intellectual property, and if they wish to charge for it, they can: for it to be otherwise would require taking their property and basically nationalizing it (while, I assume, demanding they continue to take care of it). It is frustrating that Apple has insisted on driving us to this fundamental reality, but reality it is, and the DMA isn’t going to change that.

It sure seems that this is the exact scenario that the European Commission is headed towards: demanding that Apple make its intellectual property available to third party developers on an ongoing basis without charge; again, while I think that Apple should probably do that anyways, particularly for apps that eschew the App Store entirely, I am fundamentally opposed to compelling a company to provide its services for free.

Meta and Pay or Consent

An even better example of the European Commission’s apparent dismissal of private property rights is Meta; from the Financial Times:

The European Commission, the EU’s executive body, is exercising new powers granted by the Digital Markets Act — legislation aimed at improving consumer choice and opening up markets for European start-ups to flourish. The tech giants had to comply from March this year. In preliminary findings issued on Monday, Brussels regulators said they were worried about Meta’s “pay or consent” model. Facebook and Instagram users can currently opt to use the social networks for free while consenting to data collection, or pay not to have their data shared.

The regulators said that the choice presented by Meta’s model risks giving consumers a false alternative, with the financial barrier potentially forcing them to consent to their personal data being tracked for advertising purposes.

From the European Commission’s press release:

The Commission takes the preliminary view that Meta’s “pay or consent” advertising model is not compliant with the DMA as it does not meet the necessary requirements set out under Article 5(2). In particular, Meta’s model:

1. Does not allow users to opt for a service that uses less of their personal data but is otherwise equivalent to the “personalised ads” based service.
2. Does not allow users to exercise their right to freely consent to the combination of their personal data.
   To ensure compliance with the DMA, users who do not consent should still get access to an equivalent service which uses less of their personal data, in this case for the personalisation of advertising.

Here is the problem with this characterization: there is no universe where a non-personalized version of Meta’s products are “equivalent” to a personalized version from a business perspective. Personalized ads are both far more valuable to advertisers, who only want to advertise to potential customers, not the entire Meta user base, and also a better experience for users, who get more relevant ads instead of random nonsense that isn’t pertinent to them. Indeed, personalized ads are so valuable that Eric Seufert has estimated that charging a subscription in lieu of personalized ads would cost Meta 60% of its E.U. revenue; being forced to offer completely un-personalized ads would be far more injurious.

Clearly, though, the European Commission doesn’t care about Meta or its rights to offer its products on terms it chooses: demanding a specific business model that is far less profitable (and again, a worse user experience!) is once again a de facto nationalization (continentalization?) of private property. And, as a variation on an earlier point, while I don’t agree with the demonization of personalized ads, I do recognize the European Union’s prerogative and authority to insist that Meta offer an alternative; what is problematic here is seeking to ban the fairest alternative — direct payment by consumers — and thus effectively taking Meta’s property.

Nvidia and CUDA Integration

The final example is Nvidia; from Reuters:

Nvidia is set to be charged by the French antitrust regulator for allegedly anti-competitive practices, people with direct knowledge of the matter said, making it the first enforcer to act against the computer chip maker. The French so-called statement of objections or charge sheet would follow dawn raids in the graphics cards sector in September last year, which sources said targeted Nvidia. The raids were the result of a broader inquiry into cloud computing.

The world’s largest maker of chips used both for artificial intelligence and for computer graphics has seen demand for its chips jump following the release of the generative AI application ChatGPT, triggering regulatory scrutiny on both sides of the Atlantic. The French authority, which publishes some but not all its statements of objections to companies, and Nvidia declined comment. The company in a regulatory filing last year said regulators in the European Union, China and France had asked for information on its graphic cards. The European Commission is unlikely to expand its preliminary review for now, since the French authority is looking into Nvidia, other people with direct knowledge of the matter said.

The French watchdog in a report issued last Friday on competition in generative AI cited the risk of abuse by chip providers. It voiced concerns regarding the sector’s dependence on Nvidia’s CUDA chip programming software, the only system that is 100% compatible with the GPUs that have become essential for accelerated computing. It also cited unease about Nvidia’s recent investments in AI-focused cloud service providers such as CoreWeave. Companies risk fines of as much as 10% of their global annual turnover for breaching French antitrust rules, although they can also provide concessions to stave off penalties.

I have been writing for years about Nvidia’s integrated strategy, which entails spending huge amounts of resources on the freely available CUDA software ecosystem that only runs on Nvidia chips; it is an investment that is paying off in a major way today as CUDA is the standard for creating AI applications, which provides a major moat for Nvidia’s chips (which, I would note, is counter-intuitively less meaningful even as Nvidia has dramatically risen in value). The existence of this moat — and the correspondingly high prices that Nvidia can charge — is a feature, not a bug: the chipmaker spent years and years grinding away on GPU-accelerated computing (and were frequently punished by the market), and the fact the company is profiting so handsomely from an AI revolution it made possible is exactly the sort of reward anyone interested in innovation should be happy to see.

Once again, though, European regulators don’t seem to care about incentivizing innovation, and are walking down a path that seems likely to lead to de facto continentalization of private property: the logical penalty for Nvidia’s crime of investing in CUDA could very well be the forced separation of CUDA from Nvidia chips, which is to say simply taking away Nvidia’s property; the more “moderate” punishment could be ten percent of Nvidia’s worldwide revenue, despite the fact that France — and almost certainly the E.U. as a whole — provide nowhere close to ten percent of Nvidia’s revenue.

The Worldwide Regulator

That ten percent of worldwide revenue number may sound familiar: that is the same punishment allowed under the DMA, and it’s worth examining in its own right. Specifically, it’s bizarrely high: while Nvidia doesn’t break out revenue by geography, Meta has said that ten percent of its revenue comes from the E.U; for Apple it’s only 7 percent. In other words, the European Union is threatening to fine these U.S. tech giants more money than they make in the E.U. market in a year!

The first thing to note is that the very existence of these threats should be considered outrageous by the U.S. government: another international entity is threatening to not just regulate U.S. companies within their borders (reasonable!), but to actually take revenue earned elsewhere in the world. It is very disappointing that the current administration is not only not standing up for U.S. companies, but actually appears to view the European Commission as their ally.

Second, just as Apple seems to have started to believe its rhetoric about how developers need Apple, instead of acknowledging that it needs developers as well, the European Commission seems to have bought into its spin that it is the world’s tech regulator; the fact everyone encounters cookie permission banners is even evidence that is the case!

The truth is that the E.U.’s assumed power come from the same dynamics that make U.S. tech companies so dominant. The fundamental structure of technology is that it is astronomically expensive to both develop and maintain, but those costs are more akin to capital costs like building a ship or factory; the marginal cost of serving users is zero (i.e. it costs a lot of money to serve users in the aggregate, but every additional user is zero marginal cost). If follows, then, that there is almost always benefit in figuring out how to serve more users, even if those users come with lower revenue opportunities.

A useful analogy is to the pharmaceutical industry; Tyler Cowen wrote in a provocative post on Marginal Revolution entitled What is the gravest outright mistake out there?:

I am not referring to disagreements, I mean outright mistakes held by smart, intelligent people. Let me turn over the microphone to Ariel Pakes, who may someday win a Nobel Prize:

Our calculations indicate that currently proposed U.S. policies to reduce pharmaceutical prices, though particularly beneficial for low-income and elderly populations, could dramatically reduce firms’ investment in highly welfare-improving R&D. The U.S. subsidizes the worldwide pharmaceutical market. One reason is U.S. prices are higher than elsewhere.

That is from his new NBER working paper. That is supply-side progressivism at work, but shorn of the anti-corporate mood affiliation.

I do not believe we should cancel those who want to regulate down prices on pharmaceuticals, even though likely they will kill millions over time, at least to the extent they succeed. (Supply is elastic!) But if we can like them, tolerate them, indeed welcome them into the intellectual community, we should be nice to others as well. Because the faults of the others probably are less bad than those who wish to regulate down the prices of U.S. pharmaceuticals.

Cowen’s point is that while many countries aggressively regulate the price of pharmaceuticals, you ultimately need a profit motive to invest in the massive up-front cost in developing new drugs; that profit comes from the U.S. market. The reason this all works is that the actual production of drugs is similar to technology: once the drug is approved every marginal pill is effectively zero marginal cost; this means that it is worth selling drugs at highly regulated prices, but you still need a reason to develop new drugs as well.

So it is with technology; to take the Meta example above, the company may very well be brought to heel with regards to offering a non-personalized-ads business model: Facebook and Instagram and WhatsApp are already developed, operations teams already exist, data centers are already built, so maybe a fraction of potential revenue will be worth continuing to offer those services in the E.U. Moreover, there is the matter of network effects: should Meta leave the E.U. it would make its services worse for non-E.U. users by reducing the size of the network on its services.

This is the point, though: the E.U.’s worldwide regulatory power is ultimately derived from the structure of technology, and structures can change. This is where that ten-percent-of-worldwide-revenue figure looms large: it fundamentally changes the calculus in terms of costs. Fines from a regional regulator are not the equivalent of engineering and server costs that you’re already paying, so you might as well capture pennies of extra revenue from said region; they are directly related to revenue from that region. In other words, they are more like marginal costs: the marginal cost of serving the E.U. is the expected value of the chance you will get fined more than you earned in any given year, and for big tech that price is going up.

That’s not the only cost that is going up for Apple in particular: part of the implication of the “Core Technology Fee” model is that Apple has put forth a tremendous amount of engineering effort to accomodate its platform to the DMA specifically. Or, to put it another way, Apple has already forked iOS: there is one version for the E.U., and one version for the rest of the world. This too dramatically changes the calculus: yes, every E.U. user comes in at zero marginal cost, but not the E.U. as a whole: Apple isn’t just paying the expected value of future fines, but actual real costs in terms of engineering time and overall complexity.

In short, the E.U. either has or is about to cross a critical line in terms of overplaying its hand: yes, most of tech may have been annoyed by their regulations, but the economic value of having one code base for the entire world meant that everyone put up with it (including users outside of the E.U.); once that code base splits, though — as it recently did for Apple — the calculations of whether or not to even serve E.U. users becomes that much closer; dramatically increasing potential fines far beyond what the region is worth only exacerbates the issue.

E.U. Losses

I don’t, for the record, think that either Meta or Apple or any of the other big tech companies — with the exception of Nvidia above — are going to leave Europe. What will happen more often, though, are things like this; from Bloomberg:

Apple Inc. is withholding a raft of new technologies from hundreds of millions of consumers in the European Union, citing concerns posed by the bloc’s regulatory attempts to rein in Big Tech. The company announced Friday that it would block the release of Apple Intelligence, iPhone Mirroring and SharePlay Screen Sharing from users in the EU this year, because the Digital Markets Act allegedly forces it to downgrade the security of its products and services.

“We are concerned that the interoperability requirements of the DMA could force us to compromise the integrity of our products in ways that risk user privacy and data security,” Apple said in a statement.

The EU’s DMA forces dominant technology platforms to abide by a long list of do’s and don’ts. Tech services are prohibited from favoring their own offerings over those of rivals. They’re barred from combining personal data across their different services; blocked from using information they collect from third-party merchants to compete against them; and have to allow users to download apps from rival platforms. As part of the rules, the EU has designated six of the biggest tech companies as “gatekeepers” — powerful platforms that require stricter scrutiny. In addition to Apple, that list includes Microsoft Corp., Google parent Alphabet Inc. and Facebook owner Meta Platforms Inc.

I explained in this Daily Update why this move from Apple was entirely rational, including specific provisions in the DMA that seem to prohibit the features Apple is withholding, in some cases due to interoperability requirements (does iPhone screen-sharing have to work with Windows?), and in others due to restrictions on data sharing (the DMA forbids sharing any user data held by the core platform with a new service built by the core platform); in truth, though, I don’t think Apple needs a good reason: given the calculations above it seems foolish to make any additional investments in E.U.-specific code bases.

European Commission Vice-President Margrethe Vestager was not happy, saying at Forum Europa:

I find that very interesting that they say we will now deploy AI where we’re not obliged to enable competition. I think that is that is the most sort of stunning open declaration that they know 100% that this is another way of disabling competition where they have a stronghold already.

This makes absolutely no sense: Apple not deploying AI in fact opens them up to competition, because their phones will be less fully featured than they might be otherwise! Vestager should be taking a victory lap, if her position made a shred of sense. In fact, though, she is playing the thieving fool like other Europeans before her; from 2014’s Economic Power in the Age of Abundance:

At first, or second, or even third glance, it’s hard to not shake your head at European publishers’ dysfunctional relationship with Google. Just this week a group of German publishers started legal action against the search giant, demanding 11 percent of all revenue stemming from pages that include listings from their sites. From Danny Sullivan’s excellent Search Engine Land:

German news publishers are picking up where the Belgians left off, a now not-so-proud tradition of suing Google for being included in its listings rather than choosing to opt-out. This time, the publishers want an 11% cut of Google’s revenue related to them being listed.

As Sullivan notes, Google offers clear guidelines for publisher’s who do not want to be listed, or simply do not want content cached. The problem, though, as a group of Belgian newspapers found out, is that not being in Google means a dramatic drop in traffic:

Back in 2006, Belgian news publishers sued Google over their inclusion in the Google News, demanding that Google remove them. They never had to sue; there were mechanisms in place where they could opt-out.

After winning the initial suit, Google dropped them as demanded. Then the publications, watching their traffic drop dramatically, scrambled to get back in. When they returned, they made use of the exact opt-out mechanisms (mainly just to block page caching) that were in place before their suit, which they could have used at any time.

In the case of the Belgian publishers in particular, it was difficult to understand what they were trying to accomplish. After all, isn’t the goal more page views (it certainly was in the end!)? The German publishers in this case are being a little more creative: like the Belgians before them they are alleging that Google benefits from their content, but instead of risking their traffic by leaving Google, they’re instead demanding Google give them a cut of the revenue they feel they deserve.

Vestager’s comments about Apple Intelligence are foolish like the Belgian publishers: she is mad that Apple isn’t giving her what she claims to want, which is a weaker Apple more susceptible to competition; expect this to be the case for lots of new features going forward. More importantly, expect this to be the case for lots of new companies: Apple and Meta will probably stay in the E.U. because they’re already there; it seems increasingly foolish for newer companies to ever even bother entering. That, more than anything, is why Apple and Meta and the other big tech companies won’t face competition even as they are forced to weaken their product offerings.

It is the cases above, though, that are thieving like the German publishers: it is one thing to regulate a market; it is another to straight up take a product or service on your terms, enabled by a company’s loyalty to its existing userbase. I might disagree with a lot of E.U. regulations, but I respect them and their right to make them; dictating business models or forcing a company to provide services for free, though, crosses the line from regulation to theft.

The E.U. Morality Tale

And so we arrive at another morality tale, this time about the E.U. Thierry Breten, the European Commissioner for Internal Market, tweeted late last year after the E.U. passed the AI Act:

Historic!

The EU becomes the very first continent to set clear rules for the use of AI 🇪🇺

The #AIAct is much more than a rulebook — it's a launchpad for EU startups and researchers to lead the global AI race.

The best is yet to come! 👍 pic.twitter.com/W9rths31MU

— Thierry Breton (@ThierryBreton) December 8, 2023

Here’s the problem with leading the world in regulation: you can only regulate what is built, and the E.U. doesn’t build anything pertinent to technology.1 It’s easy enough to imagine this tale being told in a few years’ time:

The erstwhile center of civilization, long-since surpassed by the United States, leverages the fundamental nature of technology to regulate the Internet for the entire world. The powers that be, though, seemingly unaware that their power rested on the zero marginal cost nature of serving their citizens, made such extreme demands on U.S. tech companies that they artificially raised the cost of serving their region beyond the expected payoff. While existing services remained in the region due to loyalty to their existing customers, the region received fewer new features and new companies never bothered to enter, raising the question: if a regulation is passed but no entity exists that is covered by the regulation, does the regulation even exist? If only the E.U. could have seen how the world — and its place in it! — had changed.

Or, to use Breton’s description, a launchpad without a rocket is just a burned out piece of concrete.

I wrote a follow-up to this Article in this Daily Update.

1. French startup Mistral is building compelling large language models; the Nvidia action in particular could be fatal to their future ↩

Stratechery is on summer break the week of July 1. There will be no Weekly Article or Updates. The next Update will be on Monday, July 8.

In addition, the next episode of Dithering will be on Tuesday, July 9 and the next episode of Sharp Tech will be on Thursday, July 11. Sharp China will also return the week of July 8.

The full Stratechery posting schedule is here.

This Article is available as a video essay on YouTube

Apple’s annual Worldwide Developer Conference keynote kicks off in a few hours, and Mark Gurman has extensive details of what will be announced in Bloomberg, including the name: “Apple Intelligence”. As John Gruber noted on Daring Fireball:

His report reads as though he’s gotten the notes from someone who’s already watched Monday’s keynote. I sort of think that’s what happened, given how much of this no one had reported before today. Bloomberg’s headline even boldly asserts “Here’s Everything Apple Plans to Show at Its AI-Focused WWDC Event”. I’m only aware of one feature for one platform that isn’t in his report, but it’s not a jaw-dropper, so I wouldn’t be surprised if it was simply beneath his threshold for newsworthiness. Look, I’m in the Apple media racket, so I know my inside-baseball obsessions are unusual, but despite all the intriguing nuggets Gurman drops in this piece, the thing I’m most insatiably curious about is how he got all this. Who spilled? By what means? It’s extraordinary. And don’t think for a second it’s a deliberate leak. Folks inside Apple are, I assure you, furious about this, and incredulous that one of their own colleagues would leak it to Gurman.

The irony of the leak being so huge is that nothing is particularly surprising: Apple is announcing and incorporating generative AI features throughout its operating systems and making them available to developers. Finally, the commentariat exclaims! Apple is in danger of falling dangerously behind! The fact they are partnering with OpenAI is evidence of how desperate they are! In fact, I would argue the opposite: Apple is not too late, they are taking the correct approach up and down the stack, and are well-positioned to be one of AI’s big winners.

Apple’s Business Model

Start with the most basic analysis of Apple’s business: despite all of the (legitimate) talk about Services revenue, Apple remains a hardware company at its core. From its inception the company has sold personal computers: the primary evolution has been that the devices have become ever more personal, from desktops to laptops to phones, even as the market as a whole has shifted from being enterprise-centric to consumer-centric, which plays to Apple’s strengths in design and the user experience benefits that come from integration.

Here’s the thing about an AI-mediated future: we will need devices! Take the classic example of the Spike Jonze movie “Her”:

Jonze’s depiction of hardware is completely unrealistic: there is not a single battery charger in the entire movie (the protagonist removes the device to sleep, and simply places it on his bedside table), or any consideration given to connectivity and the constraints that might put on the size and capability of the device in the protagonist’s ear; and yet, even then, there is a device in the protagonist’s ear, and, when the protagonist wants the AI to be able to see the outside world, he puts an iPhone-esque camera device in his pocket:

Now a Hollywood movie from 2013 is hardly dispositive about the future, but the laws of physics are; in this case the suspension of disbelief necessary to imagine a future of smarter-than-human AIs must grant that we need some sort of device for a long time to come, and as long as that is the case there is an obvious opportunity for the preeminent device maker of all time. Moreover, to the extent there is progress to be made in miniaturization, power management, and connectivity, it seems reasonable to assume that Apple will be on the forefront of bringing those advancements to market, and will be courageous enough to do so.

In other words, any analysis of Apple’s prospects in an AI world should start with the assumption that AI is a complement to Apple’s business, not disruptive. That doesn’t mean that Apple is guaranteed to succeed, of course: AI is the only foreseeable technological advancement that could provide sufficient differentiation to actually drive switching, but even there, the number of potential competitors is limited — there may only be one (more on this in a moment).

In the meantime, AI makes high-performance hardware more relevant, not less; Gurman notes that “Apple Intelligence” will only be available on Apple’s latest devices:

The new capabilities will be opt-in, meaning Apple won’t make users adopt them if they don’t want to. The company will also position them as a beta version. The processing requirements of AI will mean that users need an iPhone 15 Pro or one of the models coming out this year. If they’re using iPads or Macs, they’ll need models with an M1 chip at least.

I’m actually surprised at the M1 baseline (I thought it would be M2), but the iPhone 15 Pro limitation is probably the more meaningful one from a financial perspective, and speaks to the importance of RAM (the iPhone 15 Pro was the first iPhone to ship with 8GB of RAM, which is also the baseline for the M1). In short, this isn’t a case of Apple driving arbitrary differentiation; you really do need better hardware to run AI, which means there is the possibility of a meaningful upgrade cycle for the iPhone in particular (and higher ARPUs as well — might Apple actually start advertising RAM differences in iPhone models, since more RAM will always be better?).

The App Store and AI

One of the ironies of that phone-like device in Her being a camera is that such a device will probably not be how an AI “sees”; Humane has already shipped a camera-based device that simply clips on to your clothing, but the most compelling device to date is Meta’s Ray-Ban smart glasses:

Meta certainly has designs on AR glasses replacing your phone; shortly after acquiring Oculus, CEO Mark Zuckerberg predicted that devices mounted on your head would replace smartphones for most interactions in 10 years time. That prediction, though, was nine years ago; no one today, including Meta, predicts that smartphones will not be the most essential computing device in 2025, even though the Ray-Ban glasses are interesting.

The fact of the matter is that smartphones are nearly perfect devices for the jobs we ask them to do: they are small enough to be portable, and yet large enough to have a screen to interact with, sufficient battery life to make it through the day, and good enough connectivity; the smartphone, alongside cloud computing, represents the end of the beginning, i.e. the platform on which the future happens, as opposed to a transitory phase to a new class of disruptive devices.

In this view the app ecosystem isn’t so much a matter of lock-in as it is a natural state of affairs: of course the app interfaces to the physical world, from smart homes to transportation to media consumption, are located on the device that is with people everywhere. And, by extension, of course those devices are controlled by an oligopoly: the network effects of platforms are unrivaled; indeed, the real surprise of mobile — at least if you asked anyone in 2013, when Stratechery started — is that there are two platforms, instead of just one.

That, by extension, is why the Ray-Ban glasses come with an app, and thus have a chance of succeeding; one of Humane’s fatal flaws was their insistence that they could stand alone. Moreover, the longer that the smartphone is a prerequisite for new experiences, the more likely it is to endure; there is an analogy here to the continued relevance of music labels, which depend on the importance of back catalogs, which just so happen to expand with every release of new music. Every new experience that is built with the assumption of a smartphone extends the smartphone’s relevance that much further into the future.

There is, to be fair, a scenario where AI makes all applications obsolete with one fell swoop, but for now AI fits in the smartphone-enhancing pattern. First, to the extent that AI can be done locally, it will depend on the performance and battery life of something that is smartphone-sized at a minimum. Second, to the extent that AI is done in the cloud, it will depend on the connectivity and again battery life of something that is smartphone-sized as well. The latter, meanwhile, will come with usage costs, which is a potential tailwind for Apple’s (and Google’s) App Stores: those usage costs will be paid via credits or subscriptions which both platforms will mandate go through their in-app purchase systems, of which they will take a cut.

The third alternative is that most AI utilization happens via platform-provided APIs, which is exactly what Apple is expected to announce later today. From Gurman’s report:

Siri will be a part of the new AI push as well, with Apple planning a revamp to its voice-control service based on large language models — a core technology behind generative AI. For the first time, Siri users will be able to have precise control over individual features and actions within apps. For instance, people will be able to tell Siri to delete an email, edit a photo or summarize a news article. Over time, Apple will expand this to third-party apps and allow users to string multiple commands together into a single request. These features are unlikely to arrive until next year, however.

Platform-provided AI capabilities will not only be the easiest way for developers to incorporate these features, they will also likely be the best way, at least in terms of the overall user experience. Users will understand how to use them, because they will be “trained” by Apple’s own apps; they will likely be cheaper and more efficient, because they are leveraging Apple’s overall investment in capabilities; most importantly, at least in terms of Apple’s competitive position, they will further lock-in the underlying platform, increasing the hurdle for any alternative.

AI Infrastructure

There are two infrastructure concerns when it comes to the current state of AI. The first, and easiest to manage for Apple (at least in the short term), are so-called chatbots. On one hand, Apple is massively “behind” in terms of both building a ChatGPT-level chatbot, and also in terms of building out the necessary infrastructure to support that level of capability for its massive userbase. The reason I put “behind” in scare-quotes, though, is that Apple can easily solve its shortcoming in this area by partnering with a chatbot that already exists, which is exactly what they are doing. Again from Gurman:

The company’s new AI system will be called Apple Intelligence, and it will come to new versions of the iPhone, iPad and Mac operating systems, according to people familiar with the plans. There also will be a partnership with OpenAI that powers a ChatGPT-like chatbot.

The analogy here is to Search, another service that requires astronomical investments in both technology and infrastructure; Apple has never built and will never need to build a competitive search engine, because it owns the devices on which search happens, and thus can charge Google for the privilege of making the best search engine the default on Apple devices. This is the advantage of owning the device layer, and it is such an advantageous position that Apple can derive billions of dollars of profit at essentially zero cost.

A similar type of partnership with OpenAI will probably not be as profitable as search was; my guess is that Apple will be paying OpenAI, instead of the other way around, [UPDATE: I know longer believe this, and explain why in this post-WWDC Update] but the most important takeaway in terms of Apple’s competitive position is that they will, once again, have what is regarded as the best chatbot on their devices without having to make astronomical investments in technology and infrastructure. Moreover, this dampens the threat of OpenAI building their own device that usurps the iPhone: why would you want to buy a device that lacks the iPhone ecosystem when you can get the same level of capability on the iPhone you already have, along with all of the other aspects of the iPhone platform I noted above?

The second infrastructure concern is those API-level AI capabilities that Apple is set to extend to 3rd-party developers. Here the story is a bit fuzzier; from another Gurman report last month:

Apple Inc. will deliver some of its upcoming artificial intelligence features this year via data centers equipped with its own in-house processors, part of a sweeping effort to infuse its devices with AI capabilities. The company is placing high-end chips — similar to ones it designed for the Mac — in cloud-computing servers designed to process the most advanced AI tasks coming to Apple devices, according to people familiar with the matter. Simpler AI-related features will be processed directly on iPhones, iPads and Macs, said the people, who asked not to be identified because the plan is still under wraps.

I am intrigued to learn more about how these data centers are architected. Apple’s chips are engineered first-and-foremost for smartphones, and extended to Macs; that means they incorporate a CPU, GPU, NPU and memory into a single package. This has obvious benefits in terms of the iPhone, but there are limitations in terms of the Mac; for example, the highest end Mac Pro only has 192 GB of memory, a significant step-down from the company’s Intel Xeon-based Mac Pros, which topped out at 1.5 TB of memory. Similarly, while that top-of-the-line M2 Ultra has a 72-core GPU, it is married to a 24-core CPU; a system designed for AI processing would want far greater GPU capability without paying a “CPU tax” along the way.

In short, I don’t currently understand why Apple would build datacenters around its own chips, instead of using chips better-suited to the tasks being asked of them. Perhaps the company will announce that it has designed a new server chip, or perhaps its chips are being used in conjunction with purpose-built chips from other companies; regardless, building out the infrastructure for API-level AI features is one of the biggest challenges Apple faces, but it is a challenge that is eminently solvable, particularly since Apple controls the interface through which those capabilities will be leveraged — and when. To go back to the first Gurman article referenced above:

Apple’s AI features will be powered by its own technology and tools from OpenAI. The services will either rely on on-device processing or cloud-based computing, depending on the sophistication of the task at hand. The new operating systems will include an algorithm to determine which approach should be taken for any particular task.

Once again, we see how Apple (along with Google/Android and Microsoft/Windows) is located at the point of maximum leverage in terms of incorporating AI into consumer-facing applications: figuring out what AI applications should be run where and when is going to be a very difficult problem as long as AI performance is not “good enough”, which is likely to be the case for the foreseeable future; that means that the entity that can integrate on-device and cloud processing is going to be the best positioned to provide a platform for future applications, which is to say that the current operating system providers are the best-placed to be the platforms of the future, not just today.

Competitive Threats

Outlining Apple’s competitive position illustrates what a threat to their business must look like. In the very long run, it is certainly possible that there is an AGI that obsoletes the smartphone entirely, just as the iPhone obsoleted entire categories of consumer electronics. Yes, we will still need devices, which works in Apple’s favor, but if those devices do not depend on an app ecosystem then Apple runs the risk of being reduced to a commoditized hardware manufacturer. This, by extension, is the biggest reason to question Apple’s decision to partner with OpenAI for chatbot functionality instead of building out their own capability.

I’m skeptical, though, that this sort of wholesale transition will happen anytime soon, or ever; the reality of technology is that most new epochs layer on top of what came before, as opposed to replacing it wholesale. The Internet, for example, has been largely experienced on top of existing operating systems like Windows or iOS. Again, the most fervent AI believers may argue that I am dismissing AI’s long-term capabilities, but I think that Apple is making a reasonable bet.

It follows, then, that if I am right about the continued importance of the smartphone, that the only entity that can truly threaten Apple is Google, precisely because they have a smartphone platform and attendant ecosystem. The theory here is that Google could develop truly differentiated AI capabilities that make Android highly differentiated from the iPhone, even as Android has all of the apps and capabilities that are the price of entry to a user’s pocket in the first place.

I don’t, for the record, think that this possibility is purely theoretical; I wrote last December about Google’s True Moonshot:

What, though, if the mission statement were the moonshot all along? What if “I’m Feeling Lucky” were not a whimsical button on a spartan home page, but the default way of interacting with all of the world’s information? What if an AI Assistant were so good, and so natural, that anyone with seamless access to it simply used it all the time, without thought?

That, needless to say, is probably the only thing that truly scares Apple. Yes, Android has its advantages to iOS, but they aren’t particularly meaningful to most people, and even for those that care — like me — they are not large enough to give up on iOS’s overall superior user experience. The only thing that drives meaningful shifts in platform marketshare are paradigm shifts, and while I doubt the v1 version of Pixie would be good enough to drive switching from iPhone users, there is at least a path to where it does exactly that.

I wrote more about this possibility two weeks ago, so I don’t want to belabor the point, but this may be the biggest reason why Apple is partnering with OpenAI, and not Google: Apple might not want to build a dependency on a company might be incentivized to degrade their relative experience (a la Google Maps a decade ago), and Google might not want to give access to its potential source of long-term differentiation to the company whose business model is the clearest solution to the search company’s threat of disruption.

The disruptive potential of AI for Google is straightforward: yes, Google has massive infrastructure advantages and years of research undergirding its AI efforts, but delivering an answer instead of a set of choices is problematic both for Google’s business model, which depends on users’ choosing the winner of an auction, and for its position as an Aggregator, which depends on serving everyone in the world, regardless of their culture and beliefs.

The past few weeks have surfaced a third risk as well: Google has aggressively pushed AI results into search in response to the competitive threat from chatbots; OpenAI and Perplexity, though, aren’t upsetting user expectations when they delivery hallucinatory responses, because users already know what they are getting into when they choose to use chatbots to ask questions. Google, though, has a reputation for delivering “correct” results, which means leveraging its search distribution advantage to push AI entails significant risk to that reputation. Indeed, Google has already started to deprioritize AI results in search, moving them further down the page; that, though, at least in my personal experience, has made them significantly less useful and pushed me back towards using chatbots.

A meaningful strategic shift towards a vertical model centered around highly differentiated devices, though, solves a lot of these problems: the devices would make money in their own right (and could be high-priced because they are the best way to access Google’s differentiated AI experiences), could deliver a superior AI experience (not just via the phone, but accessories like integrated glasses, ear buds, etc), and would serve an audience that has self-selected into the experience. I remain dubious that Google will have the gumption to fully go in this direction, but it is the one possibility that should make Apple nervous.

AI Prudence

It is the other operating system provider, Microsoft, who gives further credence to Apple’s deliberative approach. Windows is not a threat to the iPhone for all of the app ecosystem reasons noted above, but Microsoft clearly sees an opportunity to leverage AI to compete with the Mac. After last month’s CoPilot+ PC event I wrote in Windows Returns:

The end result — assuming that reviewed performance measures up to Microsoft’s claims — is an array of hardware from both Microsoft and its OEM partners that is MacBook Air-esque, but, unlike Apple’s offering, actually meaningfully integrated with AI in a way that not only seems useful today, but also creates the foundation to be dramatically more useful as developers leverage Microsoft’s AI capabilities going forward. I’m not going to switch (yet), but it’s the first time I’ve been tempted; at a minimum the company set a bar for Apple to clear at next month’s WWDC.

One of the new Windows features that Microsoft touted at that event was Recall, which leverages AI to help users access everything they have seen or done on their computer in recent history. The implementation, though, turned out to be quite crude: Windows will regularly take screenshots and use local processing to index everything so that it is easily searchable. The problem is that while Microsoft stridently assured customers (and analysts!) that none of your information would be sent to the cloud, they didn’t take any measures to ensure that said data was secured locally, instead taking a dependency on Windows’ overall security. Over the intervening weeks security researchers have demonstrated why that wasn’t good enough, leading to a Microsoft announcement last week of several significant changes; from The Verge:

Microsoft says it’s making its new Recall feature in Windows 11 that screenshots everything you do on your PC an opt-in feature…Microsoft will also require Windows Hello to enable Recall, so you’ll either authenticate with your face, fingerprint, or using a PIN…This authentication will also apply to the data protection around the snapshots that Recall creates.

There are a few interesting implications in these changes:

• First, by making Recall opt-in, Microsoft is losing the opportunity to provide users with a surprise-and-delight moment when their computer finds what they were looking for; Microsoft is going to need to sell the feature to even make that experience possible.
• Second, while requiring OS-level user authentication to access Recall data is almost certainly the right choice, it’s worth pointing out that this removes the potential for 3rd-party developers to build innovative new applications on top of Recall data.

These two factors explain how this screw-up happened: Microsoft wanted to push AI as a differentiator, but the company is still at its core a developer-focused platform provider. What they announced initially solved for both, but the expectations around user data and security are such that the only entity that has sufficient trust to deliver these sorts of intimate experiences is the OS provider itself.

This is good news for Apple in two respects. First, with regards to the title of this Article, the fact it is possible to be too early with AI features, as Microsoft seemed to be in this case, implies that not having AI features does not mean you are too late. Yes, AI features could differentiate an existing platform, but they could also diminish it. Second, Apple’s orientation towards prioritizing users over developers aligns nicely with its brand promise of privacy and security: Apple would prefer to deliver new features in an integrated fashion as a matter of course; making AI not just compelling but societally acceptable may require exactly that, which means that Apple is arriving on the AI scene just in time.

I wrote a follow-up to this Article in this Daily Update.

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